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Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide with the maximum number of incidences and deaths reported from India. One of the major causes of poor survival rate associated with OSCC has been attributed to late presentation due to non-availability of a biomarker. Identification of early diagnostic biomarker will help in reducing the disease morbidity and mortality. We validated 12 salivary proteins using targeted proteomics, identified initially by relative quantification of salivary proteins on LC–MS, in OSCC patients and controls. Salivary AHSG (p = 0.0041**) and KRT6C (p = 0.002**) were upregulated in OSCC cases and AZGP1 (p ≤ 0.0001***), KLK1 (p = 0.006**) and BPIFB2 (p = 0.0061**) were downregulated. Regression modelling resulted in a significant risk prediction model (p < 0.0001***) consisting of AZGP1, AHSG and KRT6C for which ROC curve had AUC, sensitivity and specificity of 82.4%, 78% and 73.5% respectively for all OSCC cases and 87.9%, 87.5% and 73.5% respectively for late stage (T3/T4) OSCC. AZGP1, AHSG, KRT6C and BPIFB2 together resulted in ROC curve (p < 0.0001***) with AUC, sensitivity and specificity of 94%, 100% and 77.6% respectively for N0 cases while KRT6C and AZGP1 for N+ cases with ROC curve (p < 0.0001***) having AUC sensitivity and specificity of 76.8%, 73% and 69.4%. Our data aids in the identification of biomarker panels for the diagnosis of OSCC cases with a differential diagnosis between early and late-stage cases.

Motion under stochastic resetting serves to model a myriad of processes in physics and beyond, but in most cases studied to date resetting to the origin was assumed to take zero time or a time decoupled from the spatial position at the resetting moment. However, in our world, getting from one place to another always takes time and places that are further away take more time to be reached. We thus set off to extend the theory of stochastic resetting such that it would account for this inherent spatio-temporal coupling. We consider a particle that starts at the origin and follows a certain law of stochastic motion until it is interrupted at some random time. The particle then returns to the origin via a prescribed protocol. We study this model and surprisingly discover that the shape of the steady-state distribution which governs the stochastic motion phase does not depend on the return protocol. This shape invariance then gives rise to a simple, and generic, recipe for the computation of the full steady state distribution. Several case studies are analyzed and a class of processes whose steady state is completely invariant with respect to the speed of return is highlighted. For processes in this class we recover the same steady-state obtained for resetting with instantaneous returns-irrespective of whether the actual return speed is high or low. Our work significantly extends previous results on motion with stochastic resetting and is expected to find various applications in statistical, chemical, and biological physics.

Brain-like energy-efficient computing has remained elusive for neuromorphic (NM) circuits and hardware platform implementations despite decades of research. In this work we reveal the opportunity to significantly improve the energy efficiency of digital neuromorphic hardware by introducing NM circuits employing two-dimensional (2D) transition metal dichalcogenide (TMD) layered channel material-based tunnel-field-effect transistors (TFETs). Our novel leaky-integrate-fire (LIF) based digital NM circuit along with its Hebbian learning circuitry operates at a wide range of supply voltages, frequencies, and activity factors, enabling two orders of magnitude higher energy-efficient computing that is difficult to achieve with conventional material and/or device platforms, specifically the silicon-based 7 nm low-standby-power FinFET technology. Our innovative 2D-TFET based NM circuit paves the way toward brain-like energy-efficient computing that can unleash major transformations in future AI and data analytics platforms. The most successful commercial implementations of neuromorphic computing circuits are limited to digital-CMOS-circuit based approaches to date. Along this line, Pal et al. improve the energy efficiency by two orders of magnitude using two-dimensional layered material-based tunnel-field-effect transistors.

To lower expenses and environmental impacts, the integration of plug-in hybrid electric vehicles (PHEVs) into distribution networks is vital, especially in microgrid (MG) systems. Furthermore, demand-side management (DSM) measures like load shifting and price-based demand response (PBDR) help to improve the economic performance of MG systems. However, various factors influence PHEV operations, including arrival and departure times to the charging platforms, battery capacity, state of charge during arrival and departure, and overall availability in the charging platform. These considerations have a noticeable impact on the cost-effectiveness of MG operations. This paper offers a novel one-to-one-based optimisation algorithm for minimising the generating cost of an MG system while considering various PHEV operational factors and DSM tactics. The subject MG system operates in grid connected mode and is powered by a microturbine, a fuel cell, and renewables, which also facilitates the charging requirements of a PHEV. A thorough investigation is carried out by taking three varieties of PHEVs with varied battery capacity and charging/discharging power levels. Furthermore, a balanced economic emission dispatch is used to achieve the best trade-off between cost minimisation and pollution reduction, ensuring long-term MG functioning. Numerical results corroborate the improvement of load factor and reduction of peak demand using DSM methods. Shifting the availability of PHEV in the charging platform during the early hours of the day proved to be a cost-effective decision due to the lower load demand and lower electricity market price during those hours. The proposed approach improves economic efficiency while also encouraging greener energy use, making it a feasible alternative for future MG systems that incorporate PHEVs.

Background:Human mesenchymal stem cells are being used for various regenerative applications in past decades. This study chronicled a temporal profile of the transcriptional pattern and promoter methylation status of the osteogenic related gene in dental pulp stem cells (DPSCs) derived from 3-dimensional spheroid culture (3D) vis a vis 2-dimensional (2D) monolayer culture upon osteogenic induction.Methods:Biomimetic properties of osteogenesis were determined by alkaline phosphatase assay and alizarin red staining. Gene expression and promoter methylation status of osteogenic genes such as runt-related transcription factor-2, collagen1α1, osteocalcin (OCN), and DLX5 (distal-homeobox) were performed by qPCR assay and bisulfite sequencing, respectively. Furthermore, µ-Computed tomography (micro-CT) was performed to examine the new bone formation in critical-sized rat calvarial bone defect model.Results:Our results indicated a greater inclination of spheroid culture-derived DPSCs toward osteogenic lineage than the monolayer culture. The bisulfite sequencing of the promoter region of osteogenic genes revealed sustenance of low methylation levels in DPSCs during the progression of osteogenic differentiation. However, the significant difference in the methylation pattern between 2D and 3D derived DPSCs were identified only for OCN gene promoter. We observed differences in the mRNA expression pattern of epigenetic writers such as DNA methyltransferases (DNMTs) and methyl-cytosine dioxygenases (TET) between the two culture conditions. Further, the DPSC spheroids showed enhanced new bone formation ability in an animal model of bone defect compared to the cells cultivated in a 2D platform which further substantiated our in-vitro observations.Conclusion:The distinct cellular microenvironment induced changes in DNA methylation pattern and expression of epigenetic regulators such as DNMTs and TETs genes may lead to increase expression of osteogenic markers in 3D spheroid culture of DPSCs which make DPSCs spheroids suitable for osteogenic regeneration compared to monolayers.Graphical abstract

The most popular concept, demand-side management (DSM) aims to economize the operation and performance of a distribution model. The major policies of the DSM are Load-altering and load-curtailing policies, and both work to lower the system’s peak demand. The load altering or shifting employs an optimization-enabled strategy that reallocates loads of elastic types to periods of lower pricing, therefore mitigating peaks by filling in the troughs. The latter provides incentives to customers for participating and curtailing the load during peak hours. This research aims to implement integrated load shifting and curtailment algorithms inside a microgrid with a low voltage category system to distribute energy resources that are optimally scheduled, hence minimizing overall operating costs. To amplify the complexity of the work, the subject MG system also facilitates charging of vehicles of plug-in hybrid electric vehicles (PHEV) types, and hence, a smart charging strategy that uses real-time tariff (RTT) established by MG system aggregators is suggested for PHEV charging coordination to reduce the overall daily charging cost. This strategy utilizes technologies that connect vehicles to the grid and vice versa. The differential evolution (DE) algorithm was used as an optimization tool for the study. Numerical results validate that the combined load shifting cum curtailing policy proved to be more economical and efficient in reducing the TOC of the system from 889 to 716¥ and 872 to 702¥ for PHEV type I and II respectively compared to the scenarios when those policies were used individually. Additionally, the MG total operating cost (TOC) was further reduced when smart charging of PHEV was considered by changing its arrival-departure time to those hours of the day when load demand and RTT are lesser.

Background The private sector is an important yet underregulated component of the TB treatment infrastructure in India. The Joint Effort for Elimination of Tuberculosis (Project JEET) aims to link private sector TB care with the constellation of social support mechanisms available through the Indian National TB Elimination Programme (NTEP), including the provision of free fixed-dose combination (FDCs) drugs to patients. This quasi-experimental study analysed routinely collected data to determine the impact of free drugs on patient follow-ups and treatment outcomes. Methods We used data for private sector patients enrolled with Project JEET who were diagnosed with pulmonary and extrapulmonary TB between 1 January 2019 and 31 March 2020, and completed treatment by 31 December 2021. Propensity score matching was used to create a dataset to compare the number of follow-ups and proportion of successful treatment outcomes for patients on free drugs to a control group who paid out-of-pocket. 11,621 matched pairs were included in the analysis. Logistic regression and ordinary least squares regression models were used to estimate the impact of free drugs on number of follow-ups and treatment success, where latter is defined as treatment completion or cure. Results After controlling for potential confounders, patients on free drugs received on average 2.522 (95% C.I.: 2.325 to 2.719) additional follow-ups compared to patients who paid out of pocket. This equates to a 25% mean and 32% median increase in follow-ups for patients availing free drugs. For treatment success, patients receiving free drugs had 45% higher odds of a successful treatment (Odds Ratio: 1.452, 95% C.I.: 1.288 to 1.637). Conclusions Patients receiving free drugs were found to follow up with their treatment coordinator more frequently, in part likely to enable drug refilling, compared to patients who were paying out of pocket. These additional contacts would have offered opportunities to address concerns regarding side effects, provide additional treatment information, and connect with social support services, all of which subsequently contributed to patients’ continual engagement with their treatment. This potentially represents the unmeasured effect of free drugs on continual social support, which translates into a higher odds of treatment success for patients.

Performance modeling is a key issue in queuing theory and operation research. It is well-known that the length of a queue that awaits service or the time spent by a job in a queue depends not only on the service rate, but also crucially on the fluctuations in service time. The larger the fluctuations, the longer the delay becomes and hence, this is a major hindrance for the queue to operate efficiently. Various strategies have been adapted to prevent this drawback. In this perspective, we investigate the effects of one such novel strategy namely resetting or restart, an emerging concept in statistical physics and stochastic complex process, that was recently introduced to mitigate fluctuations-induced delays in queues. In particular, we show that a service resetting mechanism accompanied with an overhead time can remarkably shorten the average queue lengths and waiting times. We examine various resetting strategies and further shed light on the intricate role of the overhead times to the queuing performance. Our analysis opens up future avenues in operation research where resetting-based strategies can be universally promising.

Liver regeneration is a spontaneous process that occurs after liver injury, but acute liver failure is a complex and fatal disease which is difficult to treat. Cell-based therapies are promising alternative therapeutic approach for liver failure and different cell sources have been tested in this regard. We investigated the comparative hepatogenic potential of human bone marrow stem cells (BMSC) with stem cells derived from human dental pulp (DPSC), apical papilla (SCAP) and follicle (DFSC) during this study. Hepatogenic potential of stem cells was assessed by functional assays at both genetic and protein level. We observed higher expression of most of the hepatic markers post differentiation in DPSCs compared to other cell types. LC-MS/MS analysis of stem cell secretome revealed the presence of different proteins related to hepatogenic lineage like growth arrest specific protein 6, oncostatin M, hepatocyte growth factor receptor etc. Interactome and Reactome pathway analysis revealed the interaction of DPSC/SCAP secretome proteins and these proteins were found to be associated with various pathways involved in lipid transport and metabolism. To the best of our knowledge, this is the first study regarding detailed investigation of hepatogenic potential of BMSCs v/s DMSCs (DPSC, SCAP & DFSC) along-with secretome characterization.