Light-Shaping of Valley States.

The established paradigm to create valley states, excitations at local band extrema ("valleys"), is through selective occupation of specific valleys via circularly polarized laser pulses. Here we show a second way exists to create valley states, not by valley population imbalance but by "light-shaping" in momentum space, i.e. controlling the shape of the distribution of excited charge at each valley. While noncontrasting in valley charge, such valley states are instead characterized by a valley current, identically zero at one valley and finite and large at the other. We demonstrate that these (i) are robust to quantum decoherence, (ii) allow lossless toggling of the valley state with successive femtosecond laser pulses, and (iii) permit valley contrasting excitation both with and without a gap. Our findings open a route to robust ultrafast and switchable valleytronics in a wide scope of 2d materials, bringing closer the promise of valley-based electronics.

Nurturing Tomorrow's Antibiotic Stewards: An Innovative School Campaign to Combat Antimicrobial Resistance in Delhi.

SUMMARY: Antimicrobials are lifesaving medicines, but their misuse drives antimicrobial resistance. Schools, as educational hubs wield transformative potential in fostering responsible antimicrobial behavior among students and the broader community. An online campaign targeted Delhi schools, training teachers as master trainers who, in turn, educated 359,940 students. Significant pre- to post-test score improvements were observed among teachers (6.98-8.14; P < 0.01) and students (5.20-6.56; P < 0.01). The campaign received excellent feedback (85%), with 966 students participating in the "IDEAthon" competition. While a single session improved knowledge, continuous engagement and activities are imperative for sustained behavioral change in antibiotic usage.

Hypothyroidism presenting as a fissure tongue.

We are reporting a case of hypothyroidism presenting as fissured tongue, demonstrating significant resolution of fissure tongue upon thyroid hormone replacement therapy.

Spin vacuum switching.

Ultrafast control over the magnetic orientation of matter represents a vital element of potential future spin-based electronics ("spintronics"). While physical mechanisms underpinning spin switching are established for picosecond time scales, we here present a physical route to magnetization toggle control, i.e., multiple switching events, at <100 femtoseconds. A minority spin current injected into a ferromagnet is shown to generate rapid depopulation of the minority channel below the ground-state Fermi level, creating a minority "spin vacuum" that then drives rapid charge redistribution from the majority channel and spin switching. We demonstrate that this mechanism reproduces many of the features of recent subpicosecond switching of ferromagnetic Co/Pt multilayers and provide simple practical rules for the design of materials via tailoring the electronic density of states to optimize spin vacuum control over magnetic order.

Evaluation of AAICare®-TB sequence analysis tool for accurate diagnosis of drug-resistant tuberculosis: A comparative study with TB-Profiler and Mykrobe.

A rapid and comprehensive drug susceptibility test is essential for eliminating drug resistant tuberculosis. Next generation sequencing (NGS) based susceptibility testing is being explored as a potential substitute for the conventional phenotypic and genotypic testing methods. However, the adoption of NGS based genotypic susceptibility testing depends on the availability of simple, accurate and efficient analysis tools. This preliminary study aimed to evaluate the performance of a Mycobacterium tuberculosis (Mtb) genome analysis pipeline, AAICare®-TB, for susceptibility prediction, in comparison to two widely used gDST prediction tools, TB-Profiler and Mykrobe. This study was performed in a National Reference Laboratory in India on presumptive drug-resistant tuberculosis (DR-TB) isolates. Whole genome sequences of the 120 cultured isolates were obtained through Illumina sequencing on a MiSeq platform. Raw sequences were simultaneously analysed using the three tools. Susceptibility prediction reports thus generated, were compared to estimate the total concordance and discordance. WHO mutation catalogue (1st edition, 2021) was used as the reference standard for categorizing the mutations. In this study, AAICare®-TB was able to predict drug resistance status for First Line (Streptomycin, Isoniazid, Rifampicin, Ethambutol and Pyrazinamide) and Second Line drugs (Fluoroquinolones, Second Line Injectables and Ethionamide) in 93 samples along with lineage and hetero-resistance as per the WHO guidelines.

Predicting efficacy of antiseizure medication treatment with machine learning algorithms in North Indian population.

PURPOSE: This study aimed to develop a classifier using supervised machine learning to effectively assess the impact of clinical, demographical, and biochemical factors in accurately predicting the antiseizure medications (ASMs) treatment response in people with epilepsy (PWE). METHODS: Data was collected from 786 PWE at the Outpatient Department of Neurology, Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India from 2005 to 2015. Patients were followed up at the 2nd, 4th, 8th, and 12th month over the span of 1 year for the drugs being administered and their dosage, the serum drug levels, the frequency of seizure control, drug efficacy, the adverse drug reactions (ADRs), and their compliance to ASMs. Several features, including demographic details, medical history, and auxiliary examinations electroencephalogram (EEG) or Computed Tomography (CT) were chosen to discern between patients with distinct remission outcomes. Remission outcomes were categorized into 'good responder (GR)' and 'poor responder (PR)' based on the number of seizures experienced by the patients over the study duration. Our dataset was utilized to train seven classical machine learning algorithms i.e Extreme Gradient Boost (XGB), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) to construct classification models. RESULTS: Our research findings indicate that 1) among the seven algorithms examined, XGB and SVC demonstrated superior predictive performances of ASM treatment outcomes with an accuracy of 0.66 each and ROC-AUC scores of 0.67 (XGB) and 0.66 (SVC) in distinguishing between PR and GR patients. 2) The most influential factor in discerning PR to GR patients is a family history of seizures (no), education (literate) and multitherapy with Chi-square (χ2) values of 12.1539, 8.7232 and 13.620 respectively and odds ratio (OR) of 2.2671, 0.4467, and 1.9453 each. 3). Furthermore, our surrogate analysis revealed that the null hypothesis for both XGB and SVC was rejected at a 100 % confidence level, underscoring the significance of their predictive performance. These findings underscore the robustness and reliability of XGB and SVC in our predictive modelling framework. SIGNIFICANCE: Utilizing XG Boost and SVC-based machine learning classifier, we successfully forecasted the likelihood of a patient's response to ASM treatment, categorizing them as either PR or GR, post-completion of standard epilepsy examinations. The classifier's predictions were found to be statistically significant, suggesting their potential utility in improving treatment strategies, particularly in the personalized selection of ASM regimens for individual epilepsy patients.