Title: Scientists Grow 'Model Brain' From Stem Cells
Category: Health News
Created: 8/28/2013 2:35:00 PM
Last Editorial Review: 8/29/2013 12:00:00 AM

Title: Moderna Readies for Full Vaccine Approval, as Pfizer Submits Data on Booster Shot
Category: Health News
Created: 8/26/2021 12:00:00 AM
Last Editorial Review: 8/26/2021 12:00:00 AM

Pulmonary hypertension (PH) is highly prevalent in patients with interstitial lung disease (ILD) and is associated with increased morbidity and mortality. Widely available noninvasive screening tools are warranted to identify patients at risk for PH, especially severe PH, that could be managed at expert centres. This review summarises current evidence on noninvasive diagnostic modalities and prediction models for the timely detection of PH in patients with ILD. It critically evaluates these approaches and discusses future perspectives in the field. A comprehensive literature search was carried out in PubMed and Scopus, identifying 39 articles that fulfilled inclusion criteria. There is currently no single noninvasive test capable of accurately detecting and diagnosing PH in ILD patients. Estimated right ventricular pressure (RVSP) on Doppler echocardiography remains the single most predictive factor of PH, with other indirect echocardiographic markers increasing its diagnostic accuracy. However, RVSP can be difficult to estimate in patients due to suboptimal views from extensive lung disease. The majority of existing composite scores, including variables obtained from chest computed tomography, pulmonary function tests and cardiopulmonary exercise tests, were derived from retrospective studies, whilst lacking validation in external cohorts. Only two available scores, one based on a stepwise echocardiographic approach and the other on functional parameters, predicted the presence of PH with sufficient accuracy and used a validation cohort. Although several methodological limitations prohibit their generalisability, their use may help physicians to detect PH earlier. Further research on the potential of artificial intelligence may guide a more tailored approach, for timely PH diagnosis.

BACKGROUND:PEEP is a cornerstone treatment for children with pediatric ARDS. Unfortunately, its titration is often performed solely by evaluating oxygen saturation, which can lead to inadequate PEEP level settings and consequent adverse effects. This study aimed to assess the impact of increasing PEEP on hemodynamics, respiratory system mechanics, and oxygenation in children with ARDS.METHODS:Children receiving mechanical ventilation and on pressure-controlled volume-guaranteed mode were prospectively assessed for inclusion. PEEP was sequentially changed to 5, 12, 10, 8 cm H2O, and again to 5 cm H2O. After 10 min at each PEEP level, hemodynamic, ventilatory, and oxygenation variables were collected.RESULTS:A total of 31 subjects were included, with median age and weight of 6 months and 6.3 kg, respectively. The main reasons for pediatric ICU admission were respiratory failure caused by acute viral bronchiolitis (45%) and community-acquired pneumonia (32%). Most subjects had mild or moderate ARDS (45% and 42%, respectively), with a median (interquartile range) oxygenation index of 8.4 (5.8–12.7). Oxygen saturation improved significantly when PEEP was increased. However, although no significant changes in blood pressure were observed, the median cardiac index at PEEP of 12 cm H2O was significantly lower than that observed at any other PEEP level (P = .001). Fourteen participants (45%) experienced a reduction in cardiac index of > 10% when PEEP was increased to 12 cm H2O. Also, the estimated oxygen delivery was significantly lower, at 12 cm H2O PEEP. Finally, respiratory system compliance significantly reduced when PEEP was increased. At a PEEP of 12 cm H2O, static compliance had a median reduction of 25% in relation to the initial assessment (PEEP of 5 cm H2O).CONCLUSIONS:Although it may improve arterial oxygen saturation, inappropriately high PEEP levels may reduce cardiac output, oxygen delivery, and respiratory system compliance in pediatric subjects with ARDS with low potential for lung recruitability.

Exercise significantly alters human physiological functions, such as increasing cardiac output and muscle blood flow and decreasing glomerular filtration rate (GFR) and liver blood flow, thereby altering the absorption, distribution, metabolism, and excretion of drugs. In this study, we aimed to establish a database of human physiological parameters during exercise and to construct equations for the relationship between changes in each physiological parameter and exercise intensity, including cardiac output, organ blood flow (e.g., muscle blood flow and kidney blood flow), oxygen uptake, plasma pH and GFR, etc. The polynomial equation P = a_iHRⁱ was used for illustrating the relationship between the physiological parameters (P) and heart rate (HR), which served as an index of exercise intensity. The pharmacokinetics of midazolam, quinidine, digoxin, and lidocaine during exercise were predicted by a whole-body physiologically based pharmacokinetic (WB-PBPK) model and the developed database of physiological parameters following administration to 100 virtual subjects. The WB-PBPK model simulation results showed that most of the observed plasma drug concentrations fell within the 5^th–95^th percentiles of the simulations, and the estimated peak concentrations (C_max) and area under the curve (AUC) of drugs were also within 0.5–2.0 folds of observations. Sensitivity analysis showed that exercise intensity, exercise duration, medication time, and alterations in physiological parameters significantly affected drug pharmacokinetics and the net effect depending on drug characteristics and exercise conditions. In conclusion, the pharmacokinetics of drugs during exercise could be quantitatively predicted using the developed WB-PBPK model and database of physiological parameters.

Early detection of drug-drug interactions (DDIs) can facilitate timely drug development decisions, prevent unnecessary restrictions on patient enrollment, resulting in clinical study populations that are not representative of the indicated study population, and allow for appropriate dose adjustments to ensure safety in clinical trials. All of these factors contribute to a streamlined drug approval process and enhanced patient safety. Here we describe a new approach for early prediction of the magnitude of change in exposure for cytochrome P450 (P450) CYP3A4-related DDIs of small-molecule anticancer drugs based on the model-based extrapolation of human-CYP3A4-transgenic mice pharmacokinetics to humans. Victim drugs brigatinib and lorlatinib were evaluated with the new approach in combination with the perpetrator drugs itraconazole and rifampicin. Predictions of the magnitude of change in exposure deviated at most 0.99- to 1.31-fold from clinical trial results for inhibition with itraconazole, whereas exposure predictions for the induction with rifampicin were less accurate, with deviations of 0.22- to 0.48-fold. Results for the early prediction of DDIs and their clinical impact appear promising for CYP3A4 inhibition, but validation with more victim and perpetrator drugs is essential to evaluate the performance of the new method.

The orexigenic gut peptide ghrelin is an endogenous ligand for the growth hormone secretagogue receptor type 1a (GHSR1a). Systemic ghrelin administration has previously been shown to increase gastric motility and emptying. While these effects are known to be mediated by the vagus nerve, the cellular mechanism underlying these effects remains unclear. Therefore, the purpose of the present study was to investigate the signaling mechanism by which GHSR1a inhibits voltage-gated Ca²⁺ channels in isolated rat gastric vagal afferent neurons using whole-cell patch-clamp electrophysiology. The ghrelin pharmacological profile indicated that Ca²⁺ currents were inhibited with a log (Ic₅₀) = –2.10 ± 0.44 and a maximal inhibition of 42.8 ± 5.0%. Exposure to the GHSR1a receptor antagonist (D-Lys3)-GHRP-6 reduced ghrelin-mediated Ca²⁺ channel inhibition (29.4 ± 16.7% vs. 1.9 ± 2.5%, n = 6, P = 0.0064). Interestingly, we observed that activation of GHSR1a inhibited Ca²⁺ currents through both voltage-dependent and voltage-independent pathways. We also treated the gastric neurons with either pertussis toxin (PTX) or YM-254890 to examine whether the Ca²⁺ current inhibition was mediated by the Gα_i/o or Gα_q/11 family of subunits. Treatment with both PTX (Ca²⁺ current inhibition = 15.7 ± 10.6%, n = 8, P = 0.0327) and YM-254890 (15.2 ± 11.9%, n = 8, P = 0.0269) blocked ghrelin’s effects on Ca²⁺ currents, as compared with control neurons (34.3 ± 18.9%, n = 8). These results indicate GHSR1a can couple to both Gα_i/o and Gα_q/11 in gastric vagal afferent neurons. Overall, our findings suggest GHSR1a-mediated inhibition of Ca²⁺ currents occurs through two distinct pathways, offering necessary insights into the cellular mechanisms underlying ghrelin’s regulation of gastric vagal afferents.

Ketamine is a glutamate receptor antagonist that was developed over 50 years ago as an anesthetic agent. At subanesthetic doses, ketamine and some metabolites are analgesics and fast-acting antidepressants, presumably through targets other than glutamate receptors. We tested ketamine and its metabolites for activity as allosteric modulators of opioid receptors expressed as recombinant receptors in heterologous systems and with native receptors in rodent brain; signaling was examined by measuring GTP binding, β-arrestin recruitment, MAPK activation, and neurotransmitter release. Although micromolar concentrations of ketamine alone had weak agonist activity at μ opioid receptors, the combination of submicromolar concentrations of ketamine with endogenous opioid peptides produced robust synergistic responses with statistically significant increases in efficacies. All three opioid receptors (μ, , and ) showed synergism with submicromolar concentrations of ketamine and either methionine-enkephalin (Met-enk), leucine-enkephalin (Leu-enk), and/or dynorphin A17 (Dyn A17), albeit the extent of synergy was variable between receptors and peptides. S-ketamine exhibited higher modulatory effects compared with R-ketamine or racemic ketamine, with ~100% increase in efficacy. Importantly, the ketamine metabolite 6-hydroxynorketamine showed robust allosteric modulatory activity at μ opioid receptors; this metabolite is known to have analgesic and antidepressant activity but does not bind to glutamate receptors. Ketamine enhanced potency and efficacy of Met-enkephalin signaling both in mouse midbrain membranes and in rat ventral tegmental area neurons as determined by electrophysiology recordings in brain slices. Taken together, these findings support the hypothesis that some of the therapeutic effects of ketamine and its metabolites are mediated by directly engaging the endogenous opioid system.

The antitumor effect of cardiotonic steroids (CTS) has stimulated the search for new methods to evaluate both kinetic and thermodynamic aspects of their binding to Na⁺/K⁺-ATPase (IUBMB Enzyme Nomenclature). We propose a real-time assay based on a chromogenic substrate for phosphatase activity (pNPPase activity), using only two concentrations with an inhibitory progression curve, to obtain the association rate (k_on), dissociation rate (k_off), and equilibrium (K_i) constants of CTS for the structure-kinetics relationship in drug screening. We show that changing conditions (from ATPase to pNPPase activity) resulted in an increase of K_i of the cardenolides digitoxigenin, essentially due to a reduction of k_on. In contrast, the K_i of the structurally related bufadienolide bufalin increased much less due to the reduction of its k_off partially compensating the decrease of its k_on. When evaluating the kinetics of 15 natural and semisynthetic CTS, we observed that both k_on and k_off correlated with K_i (Spearman test), suggesting that differences in potency depend on variations of both k_on and k_off. A rhamnose in C3 of the steroidal nucleus enhanced the inhibitory potency by a reduction of k_off rather than an increase of k_on. Raising the temperature did not alter the k_off of digitoxin, generating a H (k_off) of –10.4 ± 4.3 kJ/mol, suggesting a complex dissociation mechanism. Based on a simple and inexpensive methodology, we determined the values of k_on, k_off, and K_i of the CTS and provided original kinetics and thermodynamics differences between CTS that could help the design of new compounds.

ABSTRACTWe document the effort over the last 30 years to respond to the call by women advocates at the International Conference on Population and Development for more woman-initiated single or dual-purpose contraceptive methods by developing the Caya contoured diaphragm, an innovative diaphragm designed to meet the needs of women and their partners and expand options for nonhormonal barrier contraception. We describe the complex and interrelated set of activities undertaken to develop the product using a human-centered design process and how we are working to create a corollary sustainable market. This review includes the evidence generated around improved acceptability among couples in low- and middle-income countries and depicts challenges and practical actions on how to dispel misconceptions about diaphragm use. Importantly, we share programmatic lessons learned on increasing universal access to this new sexual and reproductive health technology. Following our new model for increasing access to new and underutilized methods, Caya is now registered and being marketed in nearly 40 countries worldwide.

ABSTRACTBackground: This analysis aimed to evaluate the effectiveness of eliciting sexual partners from HIV-positive clients using the elicitation box model (where an HIV-positive index can report sexual contacts on paper and insert in a box for a health care provider to contact at a later time) compared to the conventional model (in which a health care provider elicits sexual contacts directly from clients) in Akwa Ibom, Southern Nigeria.Methods: Between March 2021 and April 2022, data were collected from index testing registers at 4 health facilities with a high volume of HIV clients currently on treatment in 4 local government areas in Akwa Ibom State. Primary outcome analyzed was the elicitation ratio (number of partners elicited per HIV-index offered index testing services). Secondary outcomes were the index testing acceptance (index HIV-positive clients accepted index testing service), testing coverage (partners tested for HIV from a list of partners elicited from HIV-index accepted index testing services), testing yield (index partners identified HIV positive from index partners HIV-tested), and linkage rate (index partners identified HIV positive and linked to antiretroviral therapy).Results: Of the total 2,705 index clients offered index testing services, 91.9% accepted, with 2,043 and 439 indexes opting for conventional elicitation and elicitation box models, respectively. A total of 3,796 sexual contacts were elicited: 2,546 using the conventional model (elicitation ratio=1:1) and 1,250 using the elicitation box model (elicitation ratio=1:3). Testing coverage was significantly higher in the conventional compared to the elicitation box model (P<.001). However, there was no significant difference in the testing yield (P=.81) and linkage rate using the conventional compared to elicitation box models (P=.13).Conclusion: The implementation of the elicitation box model resulted in an increase in partner elicitation compared to the conventional model. Increasing the testing coverage by implementing the elicitation box model should be considered.

Although acute inflammation serves essential functions in maintaining tissue homeostasis, chronic inflammation is causally linked to many diseases. Macrophages are a major cell type that orchestrates inflammatory processes. During inflammation, macrophages undergo polarization and activation, thereby mobilizing pro-inflammatory and anti-inflammatory transcriptional programs that regulate ensuing macrophage functions. Fatty acid binding protein 5 (FABP5) is a lipid chaperone highly expressed in macrophages. FABP5 deletion is implicated in driving macrophages toward an anti-inflammatory phenotype, yet signaling pathways regulated by macrophage-FABP5 have not been systematically profiled. We leveraged proteomic and phosphoproteomic approaches to characterize pathways modulated by FABP5 in M1 and M2 polarized bone marrow-derived macrophages (BMDMs). Stable isotope labeling by amino acids-based analysis of M1 and M2 polarized wild-type and FABP5 knockout BMDMs revealed numerous differentially regulated proteins and phosphoproteins. FABP5 deletion impacted downstream pathways associated with inflammation, cytokine production, oxidative stress, and kinase activity. Toll-like receptor 2 (TLR2) emerged as a novel target of FABP5 and pharmacological FABP5 inhibition blunted TLR2-mediated activation of downstream pathways, ascribing a novel role for FABP5 in TLR2 signaling. This study represents a comprehensive characterization of the impact of FABP5 deletion on the proteomic and phosphoproteomic landscape of M1 and M2 polarized BMDMs. Loss of FABP5 altered pathways implicated in inflammatory responses, macrophage function, and TLR2 signaling. This work provides a foundation for future studies seeking to investigate the therapeutic potential of FABP5 inhibition in pathophysiological states resulting from dysregulated inflammatory signaling.

Chronic pain conditions affect nearly 20% of the population in the United States. Current medical interventions, such as opioid drugs, are effective at relieving pain but are accompanied by many undesirable side effects. This is one reason increased numbers of chronic pain patients have been turning to Cannabis for pain management. Cannabis contains many bioactive chemical compounds; however, current research looking into lesser-studied minor cannabinoids in Cannabis lacks uniformity between experimental groups and/or excludes female mice from investigation. This makes it challenging to draw conclusions between experiments done with different minor cannabinoid compounds between laboratories or parse out potential sex differences that could be present. We chose five minor cannabinoids found in lower quantities within Cannabis: cannabinol (CBN), cannabidivarin (CBDV), cannabigerol (CBG), 8-tetrahydrocannabinol (8-THC), and 9-tetrahydrocannabivarin (THCV). These compounds were then tested for their cannabimimetic and pain-relieving behaviors in a cannabinoid tetrad assay and a chemotherapy-induced peripheral neuropathy (CIPN) pain model in male and female CD-1 mice. We found that the minor cannabinoids we tested differed in the cannabimimetic behaviors evoked, as well as the extent. We found that CBN, CBG, and high-dose 8-THC evoked some tetrad behaviors in both sexes, while THCV and low-dose 8-THC exhibited cannabimimetic tetrad behaviors only in females. Only CBN efficaciously relieved CIPN pain, which contrasts with reports from other researchers. Together these findings provide further clarity to the pharmacology of minor cannabinoids and suggest further investigation into their mechanism and therapeutic potential.

People with sickle cell disease (SCD) often experience chronic pain as well as unpredictable episodes of acute pain, which significantly affects their quality of life and life expectancy. Current treatment strategies for SCD-associated pain primarily rely on opioid analgesics, which have limited efficacy and cause serious adverse effects. Cannabis has emerged as a potential alternative, yet its efficacy remains uncertain. In this study, we investigated the antinociceptive effects of ⁹-tetrahydrocannabinol (THC), cannabis’ intoxicating constituent, in male HbSS mice, which express >99% human sickle hemoglobin, and male HbAA mice, which express normal human hemoglobin A, as a control. Acute THC administration (0.1–3 mg/kg^–1, i.p.) dose-dependently reduced mechanical and cold hypersensitivity in human sickle hemoglobin (HbSS) but not human normal hemoglobin A (HbAA) mice. In the tail-flick assay, THC (1 and 3 mg/kg^–1, i.p.) produced substantial antinociceptive effects in HbSS mice. By contrast, THC (1 mg/kg^–1, i.p.) did not alter anxiety-like behavior (elevated plus maze) or long-term memory (24-hour novel object recognition). Subchronic THC treatment (1 and 3 mg/kg^–1, i.p.) provided sustained relief of mechanical hypersensitivity but led to tolerance in cold hypersensitivity in HbSS mice. Together, the findings identify THC as a possible therapeutic option for the management of chronic pain in SCD. Further research is warranted to elucidate its mechanism of action and possible interaction with other cannabis constituents.

Targeting the endocannabinoid (eCB) signaling system for pain relief is an important treatment option that is only now beginning to be mechanistically explored. In this review, we focus on two recently appreciated cannabinoid-based targeting strategies, treatments with cannabidiol (CBD) and α/β-hydrolase domain containing 6 (ABHD6) inhibitors, which have the exciting potential to produce pain relief through distinct mechanisms of action and without intoxication. We review evidence on plant-derived cannabinoids for pain, with an emphasis on CBD and its multiple molecular targets expressed in pain pathways. We also discuss the function of eCB signaling in regulating pain responses and the therapeutic promises of inhibitors targeting ABHD6, a 2-arachidonoylglycerol (2-AG)-hydrolyzing enzyme. Finally, we discuss how the novel cannabinoid biosensor GRAB_eCB2.0 may be leveraged to enable the discovery of targets modulated by cannabinoids at a circuit-specific level.

The aim of this study was to validate a previously developed deep learning model in 5 independent clinical trials. The predictive performance of this model was compared with the international prognostic index (IPI) and 2 models incorporating radiomic PET/CT features (clinical PET and PET models). Methods: In total, 1,132 diffuse large B-cell lymphoma patients were included: 296 for training and 836 for external validation. The primary outcome was 2-y time to progression. The deep learning model was trained on maximum-intensity projections from PET/CT scans. The clinical PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, SUV_peak, age, and performance status. The PET model included metabolic tumor volume, maximum distance from the bulkiest lesion to another lesion, and SUV_peak. Model performance was assessed using the area under the curve (AUC) and Kaplan–Meier curves. Results: The IPI yielded an AUC of 0.60 on all external data. The deep learning model yielded a significantly higher AUC of 0.66 (P < 0.01). For each individual clinical trial, the model was consistently better than IPI. Radiomic model AUCs remained higher for all clinical trials. The deep learning and clinical PET models showed equivalent performance (AUC, 0.69; P > 0.05). The PET model yielded the highest AUC of all models (AUC, 0.71; P < 0.05). Conclusion: The deep learning model predicted outcome in all trials with a higher performance than IPI and better survival curve separation. This model can predict treatment outcome in diffuse large B-cell lymphoma without tumor delineation but at the cost of a lower prognostic performance than with radiomics.

The role of gastrin-releasing peptide receptor (GRPR) in various diseases, including cancer, has been extensively studied and has emerged as a promising therapeutic target. In this study, we successfully achieved the use of [²¹²Pb]Pb-DOTAM-GRPR1, comprising the α-particle generator, ²¹²Pb, combined with a GRPR-targeting peptide, GRPR1, in a prostate cancer model. Methods: Pharmacokinetics, toxicity, radiation dosimetry, and efficacy were assessed in GRPR-positive prostate tumor–bearing mice after intravenous administration of [²¹²Pb]Pb-DOTAM-GRPR1 (where DOTAM is 1,4,7,10-tetrakis(carbamoylmethyl)-1,4,7,10-tetraazacyclododecane). Results: Preclinical studies have shown tumor targeting of up to 5 percent injected dose per gram over 24 h, and optimization of the drug formulation and quantity has led to minimized oxidation and off-target binding, respectively. Particularly, an increase in peptide amount from 28 to 280 ng was shown to reduce off-target uptake, especially at the level of the pancreas, by about 30%. Furthermore, dosimetry studies confirmed the kidney as the dose-limiting organ, and toxicity studies revealed that a nontoxic dose of up to 1,665 kBq could be injected into mice. Efficacy studies indicated a median survival time of 9 wk in the control group, which received only a buffer solution, compared with 19 wk in the group that received 4 injections of 370 kBq at 3-wk intervals. Conclusion: Taken together, these combined data demonstrate the safety, tolerability, and efficacy of [²¹²Pb]Pb-DOTAM-GRPR1, thus warranting further exploration in clinical trials.

α-Synuclein (α-Syn) aggregation in Lewy bodies and Lewy neurites has emerged as a key pathogenetic feature in Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. Various factors, including posttranslational modifications (PTMs), can influence the propensity of α-Syn to misfold and aggregate. PTMs are biochemical modifications of a protein that occur during or after translation and are typically mediated by enzymes. PTMs modulate several characteristics of proteins including their structure, activity, localization, and stability. α-Syn undergoes various posttranslational modifications, including phosphorylation, ubiquitination, SUMOylation, acetylation, glycation, O-GlcNAcylation, nitration, oxidation, polyamination, arginylation, and truncation. Different PTMs of a protein can physically interact with one another or work together to influence a particular physiological or pathological feature in a process known as PTMs crosstalk. The development of detection techniques for the cooccurrence of PTMs in recent years has uncovered previously unappreciated mechanisms of their crosstalk. This has led to the emergence of evidence supporting an association between α-Syn PTMs crosstalk and synucleinopathies. In this review, we provide a comprehensive evaluation of α-Syn PTMs, their impact on misfolding and pathogenicity, the pharmacological means of targeting them, and their potential as biomarkers of disease. We also highlight the importance of the crosstalk between these PTMs in α-Syn function and aggregation. Insight into these PTMS and the complexities of their crosstalk can improve our understanding of the pathogenesis of synucleinopathies and identify novel targets of therapeutic potential.

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts, such as concentration-response curves, additive effects, and predictive models, are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. Although various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors, such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care.

Multiple sclerosis (MS) is a chronic inflammatory demyelinating and neurodegenerative disease of the central nervous system (CNS), with a putative autoimmune origin and complex pathogenesis. Modification of the natural history of MS by reducing relapses and slowing disability accumulation was first attained in the 1990 s with the development of the first-generation disease-modifying therapies. Glatiramer acetate (GA), a copolymer of L-alanine, L-lysine, L-glutamic acid, and L-tyrosine, was discovered due to its ability to suppress the animal model of MS, experimental autoimmune encephalomyelitis. Extensive clinical trials and long-term assessments established the efficacy and the safety of GA. Furthermore, studies of the therapeutic processes induced by GA in animal models and in MS patients indicate that GA affects various levels of the innate and the adaptive immune response, generating deviation from proinflammatory to anti-inflammatory pathways. This includes competition for binding to antigen presenting cells; driving dendritic cells, monocytes, and B-cells toward anti-inflammatory responses; and stimulating T-helper 2 and T-regulatory cells. The immune cells stimulated by GA reach the CNS and secrete in situ anti-inflammatory cytokines alleviating the pathological processes. Furthermore, cumulative findings reveal that in addition to its immunomodulatory effect, GA promotes neuroprotective repair processes such as neurotrophic factors secretion, remyelination, and neurogenesis. This review aims to provide an overview of MS pathology diagnosis and treatment as well as the diverse mechanism of action of GA.

BACKGROUND AND PURPOSE:

Prediction of aneurysm instability is crucial to guide treatment decisions and to select appropriate patients with unruptured intracranial aneurysms (IAs) for preventive treatment. High-resolution 4D MR flow imaging and 3D quantification of aneurysm morphology could offer insights and new imaging markers for aneurysm instability. In this cross-sectional study, we aim to identify 4D MR flow imaging markers for aneurysm instability by relating hemodynamics in the aneurysm sac to 3D morphologic proxy parameters for aneurysm instability.

Real gamblers play russian roulette with shotguns. That is the core concept of Buckshot Roulette, the Inscryption-looking game of blinksweat and bulletworry. It's been out for a while now but the developers have just added a fun extra - a 4-person multiplayer mode.

Read more

Put your hand up if you'd forgotten that every Assassin's Creed game is, strictly speaking, set in the present day. I know I had. That's not the actual distant past you're parkouring through. That isn't actual Renaissance architecture you're clambering on. It's a holographic Animus simulation, conjured from ancestral memories flash-frozen within your DNA - convenient, inasmuch as it means that any inconsistencies are your DNA's fault, not Ubisoft's. If the ledge-mantling animations are glitchy, that's simply because you have bad genes.

We can both be forgiven for losing sight of Assassin's Creed's modern day narrative frame. Ubisoft themselves have downplayed it since the era of Desmond Miles, the watery Peter Parker figure who served as puppetmaster protagonist for AC games up to Assassin's Creed 3. In Assassin's Creed Shadows, however, they're planning to bring back the modern day setting in a big way, though details are scanty.

Read more

Halo Infinite recently received a big update in the form of Delta Arena, a playlist that features recreations of Halo 2's most popular maps and a special third-person mode. The true highlight, though, is yet to come. And that's through an entirely different Halo game: The Master Chief Collection. Soon enough, you'll be able to play Halo 2's lost E3 demo on it, thanks to some lovely modders.

Read more

Call Of Duty: Black Ops 6 has been out for a little while already, what with me giving its multiplayer largely a thumbs up. Still, it's an ever-evolving thing and Activision have announced the game's first seasonal drop. It's a hefty one with a lot of additions, so I'll try my best to break down the good stuff. TLDR: there's some new maps for multiplayer and zombies, new modes, and a few extra bits. I'm mildly excited for more. More in this case is good.

Read more

The developers of hero shooter Overwatch 2 must have dropped a box full of old photographs while clearing the attic, spilling old snapshots of Route 66 onto the floor and getting snared in a nostalgic daze. The game is launching a "Classic" mode today that will let you play the first-person payload pusher as it (mostly) was back in 2016 when the first Overwatch launched. That means 6v6 fights, the original abilities of its heroes, and no limits to stop the entire team picking the same character.

Read more

After their initial training phase, AI algorithms can’t update and learn from new data, meaning tech companies have to keep training new models from scratch

Supremacy, a new book from tech journalist Parmy Olson, takes us inside the rise of machine learning and AI, and examines the people behind it

Researchers have managed to encode enormous amounts of information, including images, into DNA at a rate hundreds of times faster than was previously possible

Large language models can be used to scam humans, but AI is also susceptible to being scammed – and some models are more gullible than others

The software used to control a robot is normally highly adapted to its specific physical set up. But now researchers have created a single general-purpose robotic control policy that can operate robotic arms, wheeled robots, quadrupeds, and even drones.

One of the biggest challenges when it comes to applying machine learning to robotics is the paucity of data. While computer vision and natural language processing can piggyback off the vast quantities of image and text data found on the Internet, collecting robot data is costly and time-consuming.

To get around this, there have been growing efforts to pool data collected by different groups on different kinds of robots, including the Open X-Embodiment and DROID datasets. The hope is that training on diverse robotics data will lead to “positive transfer,” which refers to when skills learned from training on one task help to boost performance on another.

The problem is that robots often have very different embodiments—a term used to describe their physical layout and suite of sensors and actuators—so the data they collect can vary significantly. For instance, a robotic arm might be static, have a complex arrangement of joints and fingers, and collect video from a camera on its wrist. In contrast, a quadruped robot is regularly on the move and relies on force feedback from its legs to maneuver. The kinds of tasks and actions these machines are trained to carry out are also diverse: The arm may pick and place objects, while the quadruped needs keen navigation.

That makes training a single AI model for robots on these large collections of data challenging, says Homer Walke, a Ph.D. student at the University of California, Berkeley. So far, most attempts have either focused on data from a narrower selection of similar robots or researchers have manually tweaked data to make observations from different robots more similar. But in research to be presented at the Conference on Robot Learning (CoRL) in Munich in November, they unveiled a new model called CrossFormer that can train on data from a diverse set of robots and control them just as well as specialized control policies.

“We want to be able to train on all of this data to get the most capable robot,” says Walke. “The main advance in this paper is working out what kind of architecture works the best for accommodating all these varying inputs and outputs.”

How to control diverse robots with the same AI model

The team used the same model architecture that powers large language model, known as a transformer. In many ways, the challenge the researchers were trying to solve is not dissimilar to that facing a chatbot, says Walke. In language modeling, the AI has to to pick out similar patterns in sentences with different lengths and word orders. Robot data can also be arranged in a sequence much like a written sentence, but depending on the particular embodiment, observations and actions vary in length and order too.

“Words might appear in different locations in a sentence, but they still mean the same thing,” says Walke. “In our task, an observation image might appear in different locations in the sequence, but it’s still fundamentally an image and we still want to treat it like an image.”

UC Berkeley/Carnegie Mellon University

Most machine learning approaches work through a sequence one element at a time, but transformers can process the entire stream of data at once. This allows them to analyze the relationship between different elements and makes them better at handling sequences that are not standardized, much like the diverse data found in large robotics datasets.

Walke and his colleagues aren’t the first to train transformers on large-scale robotics data. But previous approaches have either trained solely on data from robotic arms with broadly similar embodiments or manually converted input data to a common format to make it easier to process. In contrast, CrossFormer can process images from cameras positioned above a robot, at head height or on a robotic arms wrist, as well as joint position data from both quadrupeds and robotic arms, without any tweaks.

The result is a single control policy that can operate single robotic arms, pairs of robotic arms, quadrupeds, and wheeled robots on tasks as varied as picking and placing objects, cutting sushi, and obstacle avoidance. Crucially, it matched the performance of specialized models tailored for each robot and outperformed previous approaches trained on diverse robotic data. The team even tested whether the model could control an embodiment not included in the dataset—a small quadcopter. While they simplified things by making the drone fly at a fixed altitude, CrossFormer still outperformed the previous best method.

“That was definitely pretty cool,” says Ria Doshi, an undergraduate student at Berkeley. “I think that as we scale up our policy to be able to train on even larger sets of diverse data, it’ll become easier to see this kind of zero shot transfer onto robots that have been completely unseen in the training.”

The limitations of one AI model for all robots

The team admits there’s still work to do, however. The model is too big for any of the robots’ embedded chips and instead has to be run from a server. Even then, processing times are only just fast enough to support real-time operation, and Walke admits that could break down if they scale up the model. “When you pack so much data into a model it has to be very big and that means running it for real-time control becomes difficult.”

One potential workaround would be to use an approach called distillation, says Oier Mees, a postdoctoral research at Berkley and part of the CrossFormer team. This essentially involves training a smaller model to mimic the larger model, and if successful can result in similar performance for a much smaller computational budget.

But of more importance than the computing resource problem is that the team failed to see any positive transfer in their experiments, as CrossFormer simply matched previous performance rather than exceeding it. Walke thinks progress in computer vision and natural language processing suggests that training on more data could be the key.

Others say it might not be that simple. Jeannette Bohg, a professor of robotics at Stanford University, says the ability to train on such a diverse dataset is a significant contribution. But she wonders whether part of the reason why the researchers didn’t see positive transfer is their insistence on not aligning the input data. Previous research that trained on robots with similar observation and action data has shown evidence of such cross-overs. “By getting rid of this alignment, they may have also gotten rid of this significant positive transfer that we’ve seen in other work,” Bohg says.

It’s also not clear if the approach will boost performance on tasks specific to particular embodiments or robotic applications, says Ram Ramamoorthy, a robotics professor at Edinburgh University. The work is a promising step towards helping robots capture concepts common to most robots, like “avoid this obstacle,” he says. But it may be less useful for tackling control problems specific to a particular robot, such as how to knead dough or navigate a forest, which are often the hardest to solve.

It’s become a joke to talk about Cave Story and Dark Souls when describing other games, but in the case of Momodora: Reverie Under the Moonlight, I’d be remiss not to: the series has never been shy about its influences. In Reverie, the fourth game of the Momodora series, rdein has done a fantastic job […]

The identification of a new hominin group called Denisovans was one of the most exciting discoveries in human evolution in the last decade.

The post Several Denisovan Populations Introgressed into Modern Humans Multiple Times: Study appeared first on Sci.News: Breaking Science News.

A woman's brain was scanned throughout her pregnancy, adding to the growing body of evidence that dramatic remodelling takes place in preparation for motherhood

Modern life disrupts the circadian rhythms controlling our biology – increasing our risk of developing conditions ranging from diabetes to dementia. Lynne Peeples's new book The Inner Clock explores and offers solutions

The 16GB RAM upgrade is one of many welcome, if incremental, improvements.

FrontierMath's difficult questions remain unpublished so that AI companies can't train against it.

At the recent Gartner Symposium, there was no shortage of data and insights on the evolving role of the CIO. While the

The post The Modern CIO appeared first on Gigaom.

1. PM Narendra Modi to visit Nigeria, Brazil, Guyana from November 16  The Hindu
2. PM Modi to attend G20 Summit in Brazil, visit Nigeria and Guyana from November 16-20: Full schedule  The Times of India
3. PM Modi to embark on three nation visit to Nigeria, Brazil and Guyana from 16th nov  News On AIR
4. PM Modi's Africa-Latin America trip begins Sunday  The Economic Times
5. PM Modi to embark on 3-nation tour, attend G20 Leaders Summit in Brazil next week  The Indian Express

Now is the time to check out this free total conversion of Fallout 4.

But the streaming gaming revolution "is not going to happen overnight."

But publisher leaves lucrative loophole for "early access" Patreon subscriptions.

Rivian is teaming up with Volkswagen in a $5.8 billion joint venture for EV innovation on its R2 model, which the company says will eventually be manufactured in Georgia.

A FORMER bikini model has lodged a formal complaint with the Queensland Police Service after officers accessed her personal file more than 1400 times.

Laissez les beaux-pères au chalet et vos candidats libres de vous étonner. S’ils en sont capables, ils le feront.

You humans might be amazed to know what we dogs have figured out.

Indian and multinational pharma companies are leading the charge by investing in digital transformation and aligning Indian operations with global standards. Essentially, pharmaceutical water systems are

The Subject Expert Committee (SEC), which advises the national drug regulator on approval of new drugs and clinical trials, has recommended grant of market authorisation for Dr Reddy's Laboratories' Siponimod

Oversimplification about integration of traditional and modern treatment systems often creates confusion among the stakeholders, and nobody understands the reality of the integration and what it is meant for, said Dr.