MOSARIX: Multi-crystal spectrometer in the tender x-ray range at SOLEIL synchrotron.

We have built and commissioned a novel standalone multi-crystal x-ray spectrometer (MOSARIX) in the von Hamos configuration based on highly annealed pyrolytic graphite crystals. The spectrometer is optimized for the energy range of 2-5 keV, but this range can be extended up to 20 keV by using higher reflection orders. With its nine crystals and a Pilatus detector, MOSARIX achieves exceptional detection efficiency with good resolving power (better than 4000), opening the door to study small cross section phenomena and perform fast in situ measurements. The spectrometer operates under a He atmosphere, which provides a flexible sample environment for measurements in gas, liquid, and solid phases.

Evidence of antigenic drift in the fusion machinery core of SARS-CoV-2 spike.

Antigenic drift of SARS-CoV-2 is typically defined by mutations in the N-terminal domain and receptor binding domain of spike protein. In contrast, whether antigenic drift occurs in the S2 domain remains largely elusive. Here, we perform a deep mutational scanning experiment to identify S2 mutations that affect binding of SARS-CoV-2 spike to three S2 apex public antibodies. Our results indicate that spatially diverse mutations, including D950N and Q954H, which are observed in Delta and Omicron variants, respectively, weaken the binding of spike to these antibodies. Although S2 apex antibodies are known to be nonneutralizing, we show that they confer protection in vivo through Fc-mediated effector functions. Overall, this study indicates that the S2 domain of SARS-CoV-2 spike can undergo antigenic drift, which represents a potential challenge for the development of more universal coronavirus vaccines.

CRISPR and Gene Editing: A Game-Changer in Drug Development.

CRISPR and gene editing technologies have emerged as transformative tools in medicine, offering unprecedented precision in targeting genetic disorders and revolutionizing drug development. This review explores the multifaceted impact of CRISPR across various medical domains, from hereditary diseases to infectious diseases and cancer. The potential of CRISPR in personalized medicine, therapeutic innovation, and pandemic prevention is highlighted, along with its role in reshaping traditional drug development processes. However, alongside its promise, ethical considerations loom large, particularly regarding germline editing and equitable access to treatments. The commercialization of CRISPR poses further challenges, raising questions about affordability and healthcare equity. Collaboration among scientists, policymakers, and the public is emphasized to navigate the ethical and societal implications of CRISPR responsibly. As the field advances, it is essential to ensure that the benefits of CRISPR are realized while addressing potential risks and maintaining a commitment to the well-being of future generations.

A T cell-based SARS-CoV-2 spike protein vaccine provides protection without antibodies.

SARS-CoV-2 spike-based vaccines are used to control the COVID-19 pandemic. However, emerging variants have become resistant to antibody neutralization and further mutations may lead to full resistance. We tested whether T cells alone could provide protection without antibodies. We designed a T cell-based vaccine in which SARS-CoV-2 spike sequences were rearranged and attached to ubiquitin. Immunization of mice with the vaccine induced no specific antibodies, but strong specific T cell responses. We challenged mice with SARS-CoV-2 wild-type strain or an Omicron variant after the immunization and monitored survival or viral titers in the lungs. The mice were significantly protected against death and weight loss caused by the SARS-CoV-2 wild-type strain, and the viral titers in the lungs of mice challenged with the SARS-CoV-2 wild-type strain or the Omicron variant were significantly reduced. Importantly, depletion of CD4+ or CD8+ T cells led to significant loss of the protection. Our analyses of spike protein sequences of the variants indicated that fewer than one-third presented by dominant HLA alleles were mutated and that most of the mutated epitopes were in the subunit 1 region. As the subunit 2 region is conservative, the vaccines targeting spike protein are expected to protect against future variants due to the T cell responses.

Innovative Strategies to Enhance mRNA Vaccine Delivery and Effectiveness: Mechanisms and Future Outlook.

The creation of mRNA vaccines has transformed the area of vaccination and allowed for the production of COVID-19 vaccines with previously unheard-of speed and effectiveness. The development of novel strategies to enhance the delivery and efficiency of mRNA vaccines has been motivated by the ongoing constraints of the present mRNA vaccine delivery systems. In this context, intriguing methods to get beyond these restrictions include lipid nanoparticles, self-amplifying RNA, electroporation, microneedles, and cell-targeted administration. These innovative methods could increase the effectiveness, safety, and use of mRNA vaccines, making them more efficient, effective, and broadly available. Additionally, mRNA technology may have numerous and far-reaching uses in the field of medicine, opening up fresh avenues for the diagnosis and treatment of disease. This paper gives an overview of the existing drawbacks of mRNA vaccine delivery techniques, the creative solutions created to address these drawbacks, and their prospective public health implications. The development of mRNA vaccines for illnesses other than infectious diseases and creating scalable and affordable manufacturing processes are some of the future directions for research in this area that are covered in this paper.

BREATH-Net: a novel deep learning framework for NO2 prediction using bi-directional encoder with transformer.

Air pollution poses a significant challenge in numerous urban regions, negatively affecting human well-being. Nitrogen dioxide (NO2) is a prevalent atmospheric pollutant that can potentially exacerbate respiratory ailments and cardiovascular disorders and contribute to cancer development. The present study introduces a novel approach for monitoring and predicting Delhi's nitrogen dioxide concentrations by leveraging satellite data and ground data from the Sentinel 5P satellite and monitoring stations. The research gathers satellite and monitoring data over 3 years for evaluation. Exploratory data analysis (EDA) methods are employed to comprehensively understand the data and discern any discernible patterns and trends in nitrogen dioxide levels. The data subsequently undergoes pre-processing and scaling utilizing appropriate techniques, such as MinMaxScaler, to optimize the model's performance. The proposed forecasting model uses a hybrid architecture of the Transformer and BiLSTM models called BREATH-Net. BiLSTM models exhibit a strong aptitude for effectively managing sequential data by adeptly capturing dependencies in both the forward and backward directions. Conversely, transformers excel in capturing extensive relationships over extended distances in temporal data. The results of this study will illustrate the proposed model's efficacy in predicting the levels of NO2 in Delhi. If effectively executed, this model can significantly enhance strategies for controlling urban air quality. The findings of this research show a significant improvement of RMSE = 9.06 compared to other state-of-the-art models. This study's primary objective is to contribute to mitigating respiratory health issues resulting from air pollution through satellite data and deep learning methodologies.

In silico and structural investigation of sulfonamides targeting VraSR two component system in methicillin-resistant Staphylococcus aureus.

Drug-resistant Staphylococcus aureus strains are global health concerns. Several studies have shown that these strains can develop defences against cell wall antibiotics such as ß-lactams, glycopeptides and daptomycin which target cell wall biosynthesis. The coordination of these responses have been associated with two component system (TCS) regulated by histidine kinase protein (VraS) and its cognate regulator VraR which influences the target DNA upon signal recognition. Computer-based screening methods, predictions and simulations have emerged as more efficient and quick ways to identify promising new compound leads from large databases against emerging drug targets thus allowing prediction of small select set of molecules for further validations. These combined approaches conserve valuable time and resources. Due to methicillin resistance, sulfonamide-derivative medications have been found to be effective treatment strategy to treat S. aureus infections. The current study used ligand-based virtual screening (LBVS) to identify powerful sulfonamide derivative inhibitors from an antibacterial compound library against VraSR signaling components, VraS and VraR. We identified promising sulfonamide derivative [compound 5: (4-[(1-{[(3,5-Dimethoxyphenyl)Carbamoyl]Methyl}-2,4-Dioxo-1,2,3,4-Tetrahydroquinazolin-3-Yl)Methyl]-N-[(Furan-2-Yl)Methyl]Benzamide)] with reasonable binding parameters of -31.38 kJ/mol and ΔGbind score of -294.32 kJ/mol against ATP binding domain of sensor kinase VraS. We further identified four compounds N1 (PCID83276726), N3 (PCID83276757), N9 (PCID3672584), and N10 (PCID20900589) against VraR DNA binding domain (VraRC) with ΔGbind energies of -190.27, -237.54, -165.21, and -190.88 kJ/mol, respectively. Structural and simulation analyses further suggest their stable interactions with DNA interacting residues and potential to disrupt DNA binding domain dimerization; therefore, it is prudent to further investigate and characterize them as VraR dimer disruptors and inhibit other promoter binding site. Interestingly, the discovery of drugs that target VraS and VraR may open new therapeutic avenues for drug-resistant S. aureus. These predictions based on screening, simulations and binding affinities against VraSR components hold promise for opening novel therapeutic avenues against drug-resistant S. aureus and present opportunities for repositioning efforts. These efforts aim to create analogs with enhanced potency and selectivity against two-component signaling systems that significantly contribute to virulence in MRSA or VRSA. These analyses contribute valuable insights into potential avenues for combating antibiotic-resistant S. aureus through computationally driven drug discovery strategies.Communicated by Ramaswamy H. Sarma.

Neurocognitive Functions After 6-Month Buprenorphine (Naloxone)-Based Opioid Agonist Maintenance Treatment: A Controlled Prospective Study.

BACKGROUND: Medications for opioid use disorder (OUD) may influence neurocognitive functions. Inadequate power, confounders, and practice effects limit the validity of the existing research. We examined the change in cognitive functions in patients with OUD at 6-month buprenorphine (naloxone) posttreatment and compared the cognitive performance of the buprenorphine-treated group with control subjects. METHODS: We recruited 498 patients with OUD within a week of initiating buprenorphine. Assessments were done twice-at baseline and 6 months. Those abstinent from illicit opioids and adherent to treatment (n = 199) underwent follow-up assessments. Ninety-eight non-substance-using control subjects were recruited from the community. The neurocognitive assessments comprised the Wisconsin Card Sorting Test, Iowa Gambling Task, Trail-Making Tests A and B (TMT-A and TMT-B), and verbal and visual N-Back Test. We controlled for potential effect modifiers. RESULTS: Twenty-five of the 32 test parameters significantly improved with 6 months of buprenorphine treatment; 20 parameters withstood corrections for multiple comparisons (P < 0.001). The improved test domains spread across cognitive tests: Wisconsin Card Sorting Test (perseverative errors and response, categories completed, conceptual responses), TMTs (time to complete), verbal and visual N-Back Tests (hits, omission, and total errors). After treatment, OUD (vs control subjects) had less perseverative response and error (P < 0.001) and higher conceptual response (P = 0.004) and took lesser time to complete TMT-A (P < 0.001) and TMT-B (P = 0.005). The baseline neurocognitive functions did not differ between those who retained and those who discontinued the treatment. CONCLUSION: Cognitive functions improve in patients with OUD on buprenorphine. This improvement is unlikely to be accounted for by the practice effect, selective attrition, and potential confounders.

Neurocognitive Dysfunctions in People with Concurrent Cannabis Use and Opioid Dependence: A Cross-Sectional, Controlled Study.

Cannabis and opioid co-dependence is independently associated with cognitive impairments. We examined neurocognitive dysfunctions in people with concurrent opioid dependence with cannabis dependence (OD+CD) or cannabis use (OD+CU) compared to those with only opioid dependence (OD) and healthy controls (HC). We selected adult participants, any sex, who met the diagnosis of OD (N = 268), OD+CU (N = 58), and OD + CD (N = 115). We recruited 68 education-matched HC. We administeredStandard progressive matrices (SPM), Wisconsin card sorting test (WCST), Iowa gambling task (IGT), Trail making tests A and B (TMT), and verbal and visual working memory 1-, 2-backtests. 496 (97.5%) were men, and 13 (2.5%) were women. In WCST, OD and OD+CD had significantly higher non-perseverative errors than HC. OD+CD group completed significantly lesser categories than HC. In verbal working memory 2-back, HC scored significantly fewer errors than OD and OD +CD. All patient groups, OD, OD+CU, and OD+CD, scored higher commission errors than HC in visual working memory 1-back. OD and OD+CD scored higher commission and total errors than the controls. OD+CU showed lesser error score than HC in TMT B. Cannabis and opioid co-dependence contribute to cognitive impairments, especially in working memory and executive functions.

Deep-Learning-Assisted Discriminative Detection of Vitamin B12 and Vitamin B9 by Fluorescent MoSe2 Quantum Dots.

A facile and environmentally mindful approach for the synthesis of MoSe2 QDs was developed via the hydrothermal method from bulk MoSe2. In this, the exfoliation of MoSe2 was enhanced with the aid of an intercalation agent (KOH), which could reduce the exfoliation time and increase the exfoliation efficiency to form MoSe2 QDs. We found that MoSe2 QDs display blue emission that is suitable for different applications. This fluorescence property of MoSe2 QDs was harnessed to fabricate a dual-modal sensor for the detection of both vitamin B12 (VB12) and vitamin B9 (VB9), employing fluorescence quenching. We performed a detailed study on the fluorescence quenching mechanism of both analytes. The predominant quenching mechanism for VB12 is via Förster resonance energy transfer. In contrast, the recognition of VB9 primarily relies on the inner filter effect. We applied an emerging and captivating approach to pattern recognition, the deep-learning method, which enables machines to "learn" patterns through training, eliminating the need for explicit programming of recognition methods. This attribute endows deep-learning with immense potential in the realm of sensing data analysis. Here, analyzing the array-based sensing data, the deep-learning technique, "convolution neural networks", has achieved 93% accuracy in determining the contribution of VB12 and VB9.

Adaptation of SARS-CoV-2 to ACE2H353K mice reveals new spike residues that drive mouse infection.

The Coronavirus Disease 2019 (COVID-19) pandemic continues to cause extraordinary loss of life and economic damage. Animal models of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection are needed to better understand disease pathogenesis and evaluate preventive measures and therapies. While mice are widely used to model human disease, mouse angiotensin converting enzyme 2 (ACE2) does not bind the ancestral SARS-CoV-2 spike protein to mediate viral entry. To overcome this limitation, we "humanized" mouse Ace2 using CRISPR gene editing to introduce a single amino acid substitution, H353K, predicted to facilitate S protein binding. While H353K knockin Ace2 (mACE2H353K) mice supported SARS-CoV-2 infection and replication, they exhibited minimal disease manifestations. Following 30 serial passages of ancestral SARS-CoV-2 in mACE2H353K mice, we generated and cloned a more virulent virus. A single isolate (SARS2MA-H353K) was prepared for detailed studies. In 7-11-month-old mACE2H353K mice, a 104 PFU inocula resulted in diffuse alveolar disease manifested as edema, hyaline membrane formation, and interstitial cellular infiltration/thickening. Unexpectedly, the mouse-adapted virus also infected standard BALB/c and C57BL/6 mice and caused severe disease. The mouse-adapted virus acquired five new missense mutations including two in spike (K417E, Q493K), one each in nsp4, nsp9, and M and a single nucleotide change in the 5' untranslated region. The Q493K spike mutation arose early in serial passage and is predicted to provide affinity-enhancing molecular interactions with mACE2 and further increase the stability and affinity to the receptor. This new model and mouse-adapted virus will be useful to evaluate COVID-19 disease and prophylactic and therapeutic interventions.IMPORTANCEWe developed a new mouse model with a humanized angiotensin converting enzyme 2 (ACE2) locus that preserves native regulatory elements. A single point mutation in mouse ACE2 (H353K) was sufficient to confer in vivo infection with ancestral severe acute respiratory syndrome-coronavirus-2 virus. Through in vivo serial passage, a virulent mouse-adapted strain was obtained. In aged mACE2H353K mice, the mouse-adapted strain caused diffuse alveolar disease. The mouse-adapted virus also infected standard BALB/c and C57BL/6 mice, causing severe disease. The mouse-adapted virus acquired five new missense mutations including two in spike (K417E, Q493K), one each in nsp4, nsp9, and M and a single nucleotide change in the 5' untranslated region. The Q493K spike mutation arose early in serial passage and is predicted to provide affinity-enhancing molecular interactions with mACE2 and further increase the stability and affinity to the receptor. This new model and mouse-adapted virus will be useful to evaluate COVID-19 disease and prophylactic and therapeutic interventions.

'Computational studies on coumestrol-ArlR interaction to target ArlRS signaling cascade involved in MRSA virulence'.

Two component signaling system ArlRS (Autolysis-related locus) regulates adhesion, biofilm formation and virulence in methicillin resistant Staphylococcus aureus. It consists of a histidine kinase ArlS and response regulator ArlR. ArlR is composed of a N-terminal receiver domain and DNA-binding effector domain at C-terminal. ArlR receiver domain dimerizes upon signal recognition and activates DNA binding by effector domain and subsequent virulence expression. In silico simulation and structural data suggest that coumestrol, a phytochemical found in Pueraria montana, forges a strong intermolecular interaction with residues involved in dimer formation and destabilizes ArlR dimerization, an essential conformational switch required for downstream effector domain to bind to virulent loci. Structural and energy profiles of simulated ArlR-coumestrol complexes suggest lower affinity between ArlR monomers due to structural rigidity at the dimer interface hindering the conformational rearrangements relevant for dimer formation. These analyses could be an attractive strategy to develop therapeutics and potent leads molecules response regulators of two component systems in which are involved in MRSA virulence as well as other drug-resistant pathogens.Communicated by Ramaswamy H. Sarma.

Peripancreatic tuberculosis.

Peripancreatic tuberculosis (PTB) is a very rare variant of tuberculosis and its clinical and radiological findings are similar to those of pancreatic malignancy. Diagnosis of PTB is usually incidental and is made after surgical resection. We are presenting a male patient who had complaints of prolonged fever, significant weight loss and yellowish discolouration of eyes and dark-coloured urine. Investigations revealed that there was a pancreatic mass causing obstructive jaundice. However, the aetiology of the mass, whether tubercular or malignant, was not clear. Hence, the patient was planned for endoscopic ultrasound-guided fine needle aspiration cytology. Cytology and aspirate were sent for a cartridge-based nucleic acid amplification test which revealed the presence of Mycobacterium tuberculosis, sensitive to rifampicin. The patient improved completely after treatment with antitubercular therapy.

Characterization, Antioxidant, and Antimicrobial Properties of Mulberry Lattices.

In order to find the most advantageous bioactive compounds from mulberry latex for drug development in the near future, this study was conducted to characterize and evaluate antioxidant and antimicrobial properties from four different mulberry lattices (BR-2, S-1, AR-14, and S-146). The characterization of the lattices was performed by scanning electron microscopy with energy-dispersive X-ray spectroscopy, gas chromatography coupled to mass spectroscopy, and Fourier transform infrared spectroscopy. Further, screenings of the antioxidant and antimicrobial potential of selected lattices were performed in vitro using 2,2-diphenyl-1-picrylhydrazyl assay and agar well diffusion methods, respectively. Interestingly, the outcome of the current study revealed that tested mulberry lattices contain a considerable amount of bioactive phytoconstituents, particularly antimicrobial and antioxidant compounds, as revealed by chromatographic analysis. BR-2 latex was found to have significant antioxidant activity (75%) followed by S-146 (64.6%) and AR-14 (52.9%). The maximum antimicrobial activity was found in BR-2 latex compared to other tested latex varieties. The results of this investigation showed that mulberry latex from the BR-2 type may successfully control both bacterial and fungal infections, with the added benefit of having enhanced antioxidant capabilities.

A novel approach for forecasting PM2.5 pollution in Delhi using CATALYST.

Air pollution is one of the main environmental issues in densely populated urban areas like Delhi. Predictions of the PM2.5 concentration must be accurate for pollution reduction strategies and policy actions to succeed. This research article presents a novel approach for forecasting PM2.5 pollution in Delhi by combining a pre-trained CNN model with a transformer-based model called CATALYST (Convolutional and Transformer model for Air Quality Forecasting). This proposed strategy uses a mixture of the two models. To derive attributes of the PM2.5 timeline of data, a pre-existing CNN model is utilized to transform the data into visual representations, which are analyzed subsequently. The CATALYST model is trained to predict future PM2.5 pollution levels using a sliding window training approach on extracted features. The model is utilized for analyzing temporal dependencies in PM2.5 time-series data. This model incorporates the advancements in the transformer-based architecture initially designed for natural language processing applications. CATALYST combines positional encoding with the Transformer architecture to capture intricate patterns and variations resulting from diverse meteorological, geographical, and anthropogenic factors. In addition, an innovative approach is suggested for building input-output couples, intending to address the problem of missing or partial data in environmental time-series datasets while ensuring that all training data blocks are comprehensive. On a PM2.5 dataset, we analyze the proposed CATALYST model and compare its performance with other standard time-series forecasting approaches, such as ARIMA and LSTM. The outcomes of the experiments demonstrate that the suggested model works better than conventional methods and is a potential strategy for accurately forecasting PM2.5 pollution. The applicability of CATALYST to real-world scenarios can be tested by running more experiments on real-world datasets. This can help develop efficient pollution mitigation measures, impacting public health and environmental sustainability.