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Case 18-2025: A 63-Year-Old Woman with Dyspnea on Exertion

A novel twin time series network for building energy consumption predicting

Energy consumption prediction in buildings is crucial for optimizing energy management. The latest research faces three critical challenges: (1) Insufficient temporal correlation extraction and prediction accuracy, hindering widespread adoption and application; (2) The positive impact of timestamp embedding in time series prediction under multi-mode decomposition; and (3) The issue of adaptive coupling with multi-source data. To overcome these issues, the study proposes Twin Time-Series Networks (T2SNET), which incorporates a time-embedding layer and a Temporal Convolutional Network (TCN) to extract patterns from Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), along with an adaptive fusion gate to combine energy consumption and meteorological data. The model was evaluated on datasets from university dormitories, office buildings, and school classrooms, showing significant improvements over the optimal baseline method. For instance, on the university classroom dataset, T2SNET reduced MAE by 4.56%, RMSE by 9.45%, and MAPE by 3.16% compared to the CEEMDAN-RF-LSTM model. These results highlight T2SNET’s effectiveness in predicting building energy consumption, providing a robust solution for energy management systems. The proposed method, along with baseline model code and data, has been updated and is available at https://github.com/HaileiYuan/T2SNET-Pro.git.

Disappearing Data at the U.S. Federal Government

What is effective communication in breastfeeding care? Perspectives from Latina women

Background Despite effective communication being critical to providing person-centered care, little is known of effective communication in breastfeeding care from the perspective of Latina women in the United States (U.S.) who experience breastfeeding inequities. Our study identifies (a) what constitutes effective communication and (b) which provider practices promote or hinder effective communication in the context of breastfeeding care delivered within the pregnancy, delivery, and postpartum periods to Latina women in the U.S. Methods We analyzed data from a community-engaged study that included semi-structured interviews in English or Spanish with Latina women from low-income households in Connecticut. Women were asked about communication experiences with healthcare providers during their breastfeeding care across the pregnancy, delivery, and postpartum periods. Reflexive thematic analysis was used to analyze the data and develop a framework depicting key themes. Results Of the 21 women interviewed, approximately half were 25–31 years of age (48%), were born outside of the U.S. (52%), and most had prior children (86%). We identified two themes of what constitutes effective communication in breastfeeding care to Latina women: personalized breadth and depth of breastfeeding information (theme 1) and bidirectional exchange of information with providers (theme 2). Provider use of open-ended questions that explored women’s breastfeeding experiences, goals, and challenges asked across the breastfeeding care continuum promoted effective communication. Conversely, providers who asked about breastfeeding at a single visit and/or used rushed, checklist-style questioning left women with unmet information needs and hindered effective communication. While some women preferred communication aligned with their cultural and language preferences, others appreciated providers who engaged with non-fluent Spanish, seen as a supportive gesture. Our “Framework for Effective Communication in Breastfeeding Care” illustrates our findings. Conclusion Our findings could inform provider and systems level efforts to promote more effective communication in breastfeeding care, ultimately enhancing care quality and person-centeredness.

AAV9-Mediated Gene Therapy for Infantile-Onset Pompe’s Disease

The Hippo-YAP/β-catenin signaling axis coordinates odontogenic differentiation in dental pulp stem cells: Implications for dentin-pulp regeneration

Objective This study investigated the interplay between Hippo-YAP and β-catenin signaling in regulating odontogenic differentiation of human dental pulp stem cells (DPSCs) and explored its potential implications for dentin-pulp regeneration. Methods Using lentivirus-mediated YAP overexpression/silencing, β-catenin siRNA knockdown, and pharmacological Wnt inhibition (via WIF-1), we assessed DPSC proliferation, migration, mineralization, and molecular markers (via qRT-PCR, immunofluorescence). In vivo validation employed subcutaneous transplantation of DPSC-seeded scaffolds in immunocompromised mice. Results YAP activation enhanced DPSC proliferation (1.44-fold), migration (1.39-fold), invasion (1.54-fold), and differentiation, as evidenced by elevated ALP activity (1.46-fold) and mineralization (1.36-fold). We observed transcriptional upregulation of odontogenic markers (RUNX2, DSPP, DMP1, OCN, ALP) and Wnt pathway components (β-catenin, Cyclin D1, c-Myc). Immunofluorescence revealed coordinated YAP and β-catenin expression patterns during differentiation. β-catenin silencing or Wnt inhibition abolished YAP-mediated functional enhancements and simultaneously suppressed YAP expression, partially confirming bidirectional regulation. In vivo, YAP-overexpressing DPSCs exhibited 1.27- to 1.62-fold induction of dentin-specific markers and β-catenin, whereas YAP silencing impaired these markers expression. Conclusions Our findings demonstrate that coordinated YAP and β-catenin signaling drives DPSC odontogenesis, with potential implications for dentin regeneration. Although reciprocal regulation is evident, direct molecular interactions require further validation.

Moving Past “Business as Usual” for Medical Education

A multi-domain collaborative denoising bearing fault diagnosis model based on dynamic inter-domain attention mechanism and noise-aware loss function

Rolling bearings are the core transmission components of large-scale rotating machinery such as wind power gearboxes and aviation engines, so timely and effective monitoring and diagnosis of their status are crucial to ensure the stable operation of equipment, reduce maintenance costs, and improve production efficiency. However, the noise interference in the industrial field often hides the original characteristics of the bearing fault signal, leading to the deep learning-based fault diagnosis model’s lack of diagnostic reliability in the strong industrial noise background. To address this problem, this paper proposes a multi-domain collaborative denoising diagnostic model based on dynamic inter-domain attention mechanism and noise-aware loss function. First, the model extracts high-dimensional features of bearing fault signals from multiple domains, such as time and frequency domains, aiming to enhance the richness and diversity of high-dimensional features to effectively suppress noise interference on the diagnostic results. Second, the dynamic inter-domain attention mechanism (DIDAM) is proposed, aiming to distinguish the importance of information in different signal domains and flexibly integrate them to realize more efficient and accurate multi-domain information fusion. Finally, the noise-aware loss function (NALF) is designed to avoid the phenomenon of the conduction model being prone to making wrong decisions due to excessive noise. Experimental results on two publicly available datasets, CWRU and MFPT, show that even in the extreme noise environment with SNR = –10 dB, the proposed model still achieves 81.25% and 76.36% fault diagnosis accuracies, which are better than most existing mainstream denoising models. Overall, the proposed method can still perform well under substantial noise interference, providing a new idea for intelligent bearing fault diagnosis in real industrial scenarios.

Encorafenib, Cetuximab, and mFOLFOX6 in <i>BRAF</i> -Mutated Colorectal Cancer

Two distinct host-specialized fungal species cause white-nose disease in bats

Abstract The emergence of infectious diseases, particularly those caused by fungal pathogens, poses serious threats to public health, wildlife and ecosystem stability1. Host–fungus interactions and environmental factors have been extensively examined2–4. However, the role of genetic variability in pathogens is often less well-studied, even for diseases such as white-nose in bats, which has caused one of the highest disease-driven death tolls documented in nonhuman mammals5. Previous research on white-nose disease has primarily focused on variations in disease outcomes attributed to host traits or environmental conditions6–8, but has neglected pathogen variability. Here we leverage an extensive reference collection of 5,479 fungal isolates from 27 countries to reveal that the widespread causative agent is not a single species but two sympatric cryptic species, each exhibiting host specialization. Our findings provide evidence of recombination in each species, but significant genetic differentiation across their genomes, including differences in genome organization. Both species contain geographically differentiated populations, which enabled us to identify the species introduced to North America and trace its source population to a region in Ukraine. In light of our discovery of the existence of two cryptic species of the causative agent of white-nose disease, our research underscores the need to integrate the study of pathogen variability into comprehensive disease surveillance, management and prevention strategies. This holistic approach is crucial for enhancing our understanding of diseases and implementing effective measures to prevent their spread.

Effect of crystal-to-detector distance shift on data processing in serial crystallography

Serial crystallography (SX) is an emerging technique to determine the room-temperature structure of macromolecules while minimizing any radiation damage. In SX, many crystals are continuously delivered to the X-ray interaction points. Crystal-to-detector distance (CTDD), an important data processing parameter, changes during sample delivery. However, the ways in which these variations affect the data quality remain unclear. Therefore, this study aimed to assess the impact of CTDD accuracy on SX data quality. Serial synchrotron crystallographic data of hen egg white lysozyme (HEWL) and glucose isomerase (GI) were processed at various CTDDs using three indexing algorithms. The CTDD that yielded the maximum number of indexed images differed from the optimized CTDD, and the resulting indexing trends varied depending on the algorithm used. The indexed data near the actual CTDD exhibited a Gaussian unit cell distribution pattern; however, this pattern became distorted as the CTDD deviated from the actual CTDD value, resulting in changes in the indexed unit cell dimensions. The phase problem was successfully solved even when HEWL and GI data deviated by ±10 mm from the actual CTDD, excluding one dataset. For HEWL processed via MOSFLM, structure refinement statistics maintained acceptable Rfree values despite a ± 10 mm deviation; however, data quality deteriorated with increasing deviation from the actual CTDD. Collectively, these results provide valuable insights for efficient SX data processing and interpretation.

Case 6-2025: A 62-Year-Old Man with Abdominal Pain

Factors associated with hypotension during the first hour of continuous renal replacement therapy in critically Ill patients: A prospective observational study

Purpose To investigate the factors of hemodynamic instability within the first hour of continuous renal replacement therapy (CRRT) in critically ill patients. Materials and methods A prospective observational cohort study of patients admitted to the intensive care unit (ICU) and underwent CRRT between January 17, 2024, and December 25, 2024, was conducted. The least absolute shrinkage and selection operator (LASSO) regression was used to screen potential factors and a multivariate logistic regression model was performed to determine the independent factors of hypotension within the first hour of CRRT. Results Hypotension occurred in 166 out of 435 patients (38.2%). Female (OR=0.53, 95%CI:0.3–0.89), the use of colloidal solutions (OR=0.23, 95%CI:0.12–0.46), platelet count (PLT) (OR=0.99, 95%CI:0.99–0.99), and baseline mean arterial pressure (MAP) (OR=0.95, 95%CI:0.93–0.96) were recognized as protective factors against hypotension within the first hour of CRRT in critically ill patients. Older age (OR=1.02, 95%CI:1.01–1.04), mechanical ventilation (OR=2.59, 95%CI:1.28–5.23), ultrafiltration rates of 101-200 mL/h (OR=2.04, 95%CI:1.11–3.75), international normalized ratio (INR) (OR=1.78, 95%CI:1.02–3.09), and high myoglobin level (OR=1.01, 95%CI:1.01–1.01) were identified as significant risk factors. Baseline MAP and mechanical ventilation are the most important predictors of hypotension within the first hour of CRRT in critically ill patients. Conclusions The incidence of hypotension within the first hour of CRRT in critically ill patients was 38.2%. Female, PLT, baseline MAP are protective factors, and age, mechanical ventilation, ultrafiltration rate of 101-200ml/h, INR, myoglobin are risk factors for hypotension within the first hour of CRRT in critically ill patients.

Incidence of Scrub Typhus in Rural South India

Shifting research focus comes with the risk of reduced impact

Can digital transformation enhance labor productivity in enterprises: An analysis from the perspective of business process reengineering

From the perspective of business process reengineering, this paper analyzes the impact of digital transformation on labor productivity in enterprises and its underlying mechanisms. The study finds that digital transformation significantly enhances labor productivity in enterprises, with both the application of digital technologies and innovation in digital technology scenarios having a notable positive effect. Furthermore, digital transformation improves labor productivity mainly by optimizing production management processes, reducing human resource redundancy, enhancing the efficiency of human resource utilization, and improving internal control mechanisms to enhance decision-making efficiency. The effects of digital transformation on labor productivity are more pronounced in non-state-owned enterprises and enterprises that are highly dependent on their industrial chains. Further analysis shows that the level of industry monopoly and the overall digitalization level of the industry play a moderating role in the process by which digital transformation affects labor productivity.

Tirzepatide for Obesity Treatment and Diabetes Prevention

Investigation on the influence of airflow on the acoustic performance of a single–Cylinder diesel engine exhaust muffler

The airflow in the exhaust muffler can affect the sound propagation characteristics. In this work, For a single-cylinder diesel engine, an experimental bench was set up, and the accuracy of the simulation model was verified through the mutual comparison between the experimental data and the simulation results. the transmission loss of exhaust muffler of a single-cylinder diesel engine is analyzed numerically. In addition, the influence of airflow on the acoustic performance of muffler is studied and analyzed in detail. Finally, a finite element method, namely automatic matched layer (AML) is used to simulate the anechoic boundary conditions, and the value of transmission loss of muffler with and without airflow is calculated. Results demonstrate that: Considering the influence of airflow, the transmission loss value of the muffler shows the obvious increase in the low-frequency domain of 0–2000 Hz, particularly below 1000 Hz, with difference up to 50 dB and an average of approx. 30dB. Nevertheless, the airflow has minimal influence on transmission loss in the medium-high frequency of 2000–7000 Hz. The acoustic performance is greatly affected by the internal fluid flow, and the fluid flow is beneficial to improving the acoustic performance of mufflers, especially in the low-frequency domain. Additionally, the change of transmission loss curve is more complex when the influence of airflow is considered. The research results of this work provide a more accurate prediction method on the transmission loss in practical application, and of reference significance for future studies on the acoustic performance of mufflers.

Generative AI in Medicine — Evaluating Progress and Challenges

Neurologists’ perspectives on management challenges and mitigation strategies for Parkinson’s disease patients: A qualitative study in Iraq

Background Parkinson’s disease (PD) is currently the fastest-growing neurological disorder in the world. Patients with PD face numerous challenges in managing their chronic condition, particularly in countries with scarce healthcare infrastructure. Objective This qualitative study aimed to delve into neurologists’ perspectives on challenges and gaps in the Iraqi healthcare system that influence the management of PD, as well as strategies to mitigate these obstacles. Method Semi-structured interviews were conducted with neurologists from five different Iraqi provinces, working in both hospitals and private neurology clinics, between November 2024 and January 2025. A thematic analysis approach was employed to identify the main challenges and gaps in the healthcare system, along with potential mitigation strategies for improving PD management in Iraq. Results Fourteen neurologists participated in this study. Among them, 71.4% identified medication adherence as a major challenge, followed by motor symptoms (64.3%). Physical dependency due to motor impairment was the most reported limitation affecting daily life and disease management. Neurologists cited several factors influencing patients’ knowledge, including educational background, depression or family support, were mentioned by neurologists. Significant gaps in the Iraqi healthcare system were noted, such as the lack of specialized abnormal movement disorders clinics, multidisciplinary collaboration, a universal healthcare system, insufficient public PD awareness, and the presence of unregulated pharmaceuticals in the market. Conclusion Addressing these challenges requires policy-driven reforms, enhanced regulatory oversight and the integration of multidisciplinary care frameworks to optimize the management of individuals with PD. Strengthening patient education and professional training programs could further raise public awareness and improve care quality. Future research should focus on evaluating the effects of these proposed interventions on clinical outcomes for PD patients and promoting more patient-centered care for individuals with PD in Iraq.