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Abstract Subcellular compartmentalization of metabolic enzymes establishes a unique metabolic environment that elicits specific cellular functions. Indeed, the nuclear translocation of certain metabolic enzymes is required for epigenetic regulation and gene expression control. Here, we show that the nuclear localization of the mitochondrial enzyme methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) ensures mitosis progression. Nuclear MTHFD2 interacts with proteins involved in mitosis regulation and centromere stability, including the methyltransferases KMT5A and DNMT3B. Loss of MTHFD2 induces severe methylation defects and impedes correct mitosis completion. MTHFD2 deficient cells display chromosome congression and segregation defects and accumulate chromosomal aberrations. Blocking the catalytic nuclear function of MTHFD2 recapitulates the phenotype observed in MTHFD2 deficient cells, whereas restricting MTHFD2 to the nucleus is sufficient to ensure correct mitotic progression. Our discovery uncovers a nuclear role for MTHFD2, supporting the notion that translocation of metabolic enzymes to the nucleus is required to meet precise chromatin needs.

Abstract Nuclear metabolism and DNA damage response are intertwined processes, but the precise molecular links remain elusive. Here, we explore this crosstalk using triple-negative breast cancer (TNBC) as a model, a subtype often prone to DNA damage accumulation. We show that the de novo purine synthesis enzyme IMPDH2 is enriched on chromatin in TNBC compared to other subtypes. IMPDH2 chromatin localization is DNA damage dependent, and IMPDH2 repression leads to DNA damage accumulation. On chromatin, IMPDH2 interacts with and modulates PARP1 activity by controlling the nuclear availability of NAD+ to fine-tune the DNA damage response. However, when IMPDH2 is restricted to the nucleus, it depletes nuclear NAD+, leading to PARP1 cleavage and cell death. Our study identifies a non-canonical nuclear role for IMPDH2, acting as a convergence point of nuclear metabolism and DNA damage response.

Abstract No pan‐India‐specific guidelines exist for the management of urological cancers. Although western guidelines are useful for informing management strategies, they do not account for the nuances of management in the Indian context. A modified Delphi method was used to provide a framework for the systematic development of India‐centric guidelines for the management of three uro‐oncology disease states: small renal masses, non‐muscle invasive bladder cancer and high‐risk/locally advanced prostate cancer.

Abstract Noisy intermediate-scale quantum (NISQ) computers provide a new experimental platform for investigating the behaviour of complex quantum systems. We show that currently available NISQ devices can be used for versatile quantum simulations of chaotic systems. We introduce a classical-quantum hybrid approach for exploring the dynamics of the chaotic quantum kicked top (QKT) on a quantum computer. The programmability of this approach allows us to experimentally explore a broad range of QKT chaoticity parameter regimes inaccessible to previous studies. Furthermore, the number of gates in our simulation does not increase with the number of kicks, thus making it possible to study the QKT evolution for arbitrary number of kicks without fidelity loss. Using a publicly accessible NISQ computer (IBMQ), we observe periodicities in the evolution of the 2-qubit QKT, as well as signatures of chaos in the time-averaged 2-qubit entanglement. We also demonstrate a connection between entanglement and delocalization in the 2-qubit QKT, confirming theoretical predictions.

Abstract Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage.

Abstract Background As South Africa’s population rapidly ages, the burden of non-communicable diseases and the challenges of performing daily care activities among older individuals are increasing. This study investigates trends in self-reported difficulties with daily care activities among older adults in South Africa and examines the association between these difficulties, sociodemographic factors, and chronic conditions. Methods The study used cross-sectional data from ten rounds of South Africa General Household Survey (2012–2021). Sample population included 26,362 men and 42,400 women aged 60 years and above. The outcome measure was assessed by self-reported difficulty in performing basic care activities such as washing or dressing. Results Between 2012 and 2021, the percentage of participants reporting “A lot of difficulty” increased by 79.75%, “Some difficulty” increased by 112.11%, while “Unable to do” decreased by 8.97%. The risk of self-care difficulties was higher for men (RR = 1.11, 95% CI = 1.02, 1.20) and increased with age: RR = 1.24 (95% CI = 1.08, 1.42) for ages 65–69; RR = 2.27 (95% CI = 2.00, 2.58) for ages 70–74; and RR = 5.65 (95% CI = 5.08, 6.28) for ages 75+. Not being currently married (RR = 1.65, 95% CI = 1.51, 1.79), being of African/Black (RR = 1.21, 95% CI = 1.06, 1.38) or Coloured race (RR = 1.41, 95% CI = 1.21, 1.65), and having diabetes (RR = 1.44, 95% CI = 1.34, 1.55), hypertension (RR = 1.35, 95% CI = 1.26, 1.45), or asthma (RR = 1.30, 95% CI = 1.14, 1.48) were also associated with a higher risk of self-care difficulties. Conclusion There was a notable increase in the proportion of participants reporting significant difficulty and some difficulty in performing self-care tasks, while a decrease was observed in the category of participants unable to do such tasks. Health policies should prioritize the specific needs of vulnerable sociodemographic and health groups, considering their increased risk of self-care difficulties.

Introduction Undergraduate medical students often lack the soft skill of empathy which is considered as an important attribute for doctors. Hence, this study was conducted to improve the empathy skills among undergraduate students of a medical college in Kolkata. Methods A quasi-experimental study was conducted in the Rural Health Training Centre of a medical college in Kolkata among the undergraduate medical students during community medicine posting. Students were assessed for baseline empathy by Jefferson’s Scale of Empathy (student’s version) (JSE-S). Baseline perception was taken from geriatric persons about the empathy level of medical undergraduates using Jefferson’s Scale of Patient’s Perception about Physician’s Empathy (JSPPE). Training module was prepared, validated and imparted. Post-training score was obtained using the scales for students as well as geriatric persons. Data were entered in MS Excel and analysed using STATA MP16. Results Total of 93 students participated. Overall Mean ± SD of pre-score of JES-S was 102.03 ± 13.02, and post-score was 109.03 ± 14.57; the difference was found to be statistically significant (P < 0.001). With respect to JSPPE, score difference was found to be statistically significant (P < 0.001). Significant difference was present in scores between the students who prefer people-oriented specialities compared to the technology-oriented specialities (P < 0.05). Increase in mean empathy score was significantly higher among female students than their male counterparts (P = 0.01). Conclusion(s) The study shows the effectiveness of training module in increasing empathy score among medical undergraduates which is the need of the hour to establish the proper building block of doctor-patient relationship.

Abstract Metal-organic frameworks (MOFs) are highly attractive porous materials with applications spanning the fields of chemistry, physics, biology, and engineering. Their exceptional porosity and structural flexibility have led to widespread use in catalysis, separation, biomedicine, and electrochemistry. Currently, most MOFs are synthesized under equilibrium liquid-phase reaction conditions. Here we show a general and versatile non-equilibrium flame aerosol synthesis of MOFs, in which rapid kinetics of MOF formation yields two distinct classes of MOFs, nano-crystalline MOFs and amorphous MOFs. A key advantage of this far-from-equilibrium synthesis is integration of different metal cations within a single MOF phase, even when this is thermodynamically unfavorable. This can, for example, produce single-atom catalysts and bimetallic MOFs of arbitrary metal pairs. Moreover, we demonstrate that dopant metals (e.g., Pt, Pd) can be exsolved from the MOF framework by reduction, forming nanoclusters anchored on the MOF. A prototypical example of such a material exhibited outstanding performance as a CO oxidation catalyst. This general synthesis route opens new opportunities in MOF design and applications across diverse fields and is inherently scalable for continuous production at industrial scales.

Recently time series forecasting has become one of the prime application areas of climatology, economics and industries. Many research works are conducted to forecast the time series more accurately. But few of them are concentrated on predicting the time series over an extended future horizon, and there is also a scope to improve their forecasting accuracy. This work proposes a multi-step-ahead foresting method to produce a stable and accurate forecasting result for the extended future horizon. Firstly, a deep learning-based forecasting model is proposed to predict the time series. Secondly, a fuzzy time series-based error correction model is implemented to enhance the prediction performance of the deep learning model. Here to optimize all the fuzzy time series (FTS) parameters in an integrated way, an integrated butterfly optimization (FTS-IBO) algorithm is proposed. In this study, two different types of real-world multivariate time series datasets are used to analyze the forecasting performance of the proposed model. The performance of the proposed FTS-IBO algorithm is compared with the traditional butterfly optimization (FTS-BO) algorithm. The experimental results show that the FTS-IBO technique is superior to the FTS-BO technique. The forecasting performance of the proposed model has also compared the other state-of-the-art models, and the simulation results exhibit that the proposed model produces a more accurate prediction performance for multi-step-ahead time series forecasting problems compared to other models.

Abstract Background Pulmonary oedema is a feared and difficult to predict complication of severe malaria that can emerge after start of antimalarial treatment. Proinflammatory mediators are thought to play a central role in its pathogenesis. Methods An exploratory study was conducted to evaluate the predictive capacity of biomarkers for development of clinical pulmonary oedema in patients with severe falciparum malaria at two hospitals in Bangladesh. Plasma concentrations of interleukin-6 (IL-6), IL-8, tumour necrosis factor (TNF), soluble Receptor of Advanced Glycation End-products (sRAGE), surfactant protein-D (SP-D), club cell secretory protein (CC16), and Krebs von den Lungen-6 (KL-6) on admission were compared with healthy controls. Correlations between these biomarker and plasma lactate and Plasmodium falciparum histidine-rich protein 2 (PfHRP2) levels were evaluated. Receiver Operating Characteristic (ROC) curves were constructed to assess the predictive capacity for clinical pulmonary oedema of the biomarkers of interest. Results Of 106 screened patients with falciparum malaria, 56 were classified as having severe malaria with a mortality rate of 29%. Nine (16%) patients developed clinical pulmonary oedema after admission. Plasma levels of the biomarkers of interest were higher in patients compared to healthy controls. IL-6, IL-8, TNF, sRAGE, and CC16 levels correlated well with plasma PfHRP2 levels (r s = 0.39; P = 0.004, r s = 0.43; P = 0.001, r s = 0.54; P

The 25th Annual Western Canadian Gastrointestinal Cancer Consensus Conference (WCGCCC) was held in Winnipeg, Manitoba, on 26–27 October 2023. The WCGCCC is an interactive multidisciplinary conference that was attended by healthcare professionals from across Western Canada (British Columbia, Alberta, and Manitoba) who are involved in the care of patients with gastrointestinal cancer. Surgical, medical, and radiation oncologists; pathologists; oncology nurses; pharmacists; and a family physician in oncology (FPO) participated in presentation and discussion sessions for the purpose of developing the recommendations presented here. This consensus statement addresses current issues in the management of gastroesophageal cancers.

Abstract Background Despite global efforts to achieve zero hunger, food insecurity remains a critical challenge in several African countries, including Cameroon. This study aims to identify sociodemographic predictors of food insecurity across Cameroon through a comprehensive, nationwide cross-sectional analysis. Methods Data for this study were drawn from the 2018 Cameroon Demographic and Health Survey (CDHS). Food insecurity levels were evaluated using the Food Insecurity Experience Scale (FIES). To estimate the probabilities of moderate and severe food insecurity among different sociodemographic groups, logistic regression models were applied, with results expressed as average marginal effects (AME). Results The findings reveal that approximately 24.86% of participants experienced moderate food insecurity, while 28.96% faced severe food insecurity. Among men, the proportion experiencing severe food insecurity was 38.8%, compared to 24.3% for women. Multivariable regression analysis showed that severe food insecurity was less likely among women [AME = 0.84, 95% CI = 0.83,0.86], Muslims [AME = 0.91, 95%CI = 0.90,0.93], individuals with higher education levels (secondary education: AME = 0.93, 95%CI = 0.90,0.95; higher education: AME = 0.87, 95%CI = 0.85,0.90), those owning land (either alone or jointly) [AME = 0.92, 95%CI = 0.89,0.96], wealthiest households (moderate food insecurity: AME = 0.91, 95%CI = 0.88,0.93; severe food insecurity: AME = 0.73, 95%CI = 0.70,0.75), female-headed households [AME = 0.97, 95%CI = 0.96,0.99], and rural residents [AME = 0.97, 95%CI = 0.95,0.98]. Conclusions These findings underscore that food insecurity affects a substantial portion of the Cameroonian population, with certain sociodemographic groups more vulnerable than others. This study proposes targeted policy recommendations to address food insecurity in Cameroon, including social assistance programs for at-risk groups, investments in socioeconomic empowerment, improvements in agricultural productivity, and ongoing research to guide evidence-based interventions.

Background: The COVID-19 pandemic caused an unprecedented mid-semester transition to virtual learning. Instructors and students had to adapt to new ways of delivering and receiving course material. Objective: The present investigation examined whether course format and sense of belongingness were associated with learning satisfaction during the COVID-19 pandemic, as well as racial/ethnic or gender identity differences in academic experiences during this time. The current study also explored student perceptions of instructor support, changes in workload, and changes in learning. Method: Undergraduate students (N = 157) responded to quantitative and qualitative items regarding their academic experiences during the first semester of the pandemic in an online survey. Results: Blended courses were associated with poorer outcomes than solely synchronous and asynchronous courses. There were no racial differences in academic experiences; however, women had more positive academic experiences than men. Greater academic and campus belongingness predicted better academic experiences. Students perceived clear, frequent instructor communication as vital to their success. Conclusion: Students' experiences with virtual learning varied depending on instructor and student factors.

Perceptual learning is the process by which experience alters how incoming sensory information is processed by the brain to give rise to behavior—it is critical for how humans educate children, train experts, treat diseases, and promote health and well-being throughout the lifespan. Knowledge of perceptual learning requires basic and applied research in humans and nonhuman animal models, which informs strategic targets for advancing applications. Commercial products to induce perceptual learning are proliferating rapidly with limited regulation (e.g., for rehabilitation), while at the same time basic science is increasingly restricted by changing regulations (such as new granting-agency definitions of clinical trials). Realizing the full potential of perceptual learning requires balancing basic and translational science to advance new knowledge, while serving and protecting consumers. Reforms can promote open, accessible, and representative research, and the translation of this research to applications across different sectors of society.