Browse Articles

Diagnoses and critical care outcomes in a rural Tanzanian high dependency unit: A prospective cohort study

Background Data on rural sub-Saharan African high-dependency units (HDU) are lacking. We describe patient’s characteristics, diagnoses, and outcomes of patients admitted to a Tanzanian HDU, and identified factors associated with in-hospital mortality. Methods This prospective single-center cohort study was conducted in the HDU of a Tanzanian rural referral hospital. All patients admitted to the HDU were eligible. Descriptive analyses, and univariate and multivariate modeling to identify predictors of in-hospital mortality were done. Kaplan-Meier survival curves were employed to estimate mortality rates over time. The area under the receiver operating characteristic curve was used to assess the predictive accuracy of early warning scores. Results From April 4th 2023 to March 29th 2024, 491 patients were included and followed-up until hospital discharge. Median age was 46 years (IQR 29−65); 259 (53%) were females. Most common diagnoses were sepsis (N = 96, 20%), arterial hypertension (N = 91, 19%), diabetes mellitus (N = 84, 17%), acute kidney injury (N = 66, 13%), decompensated heart failure (N = 64, 13%), aspiration pneumonia (N = 60, 12%), and stroke (N = 59, 12%). Mortality during HDU- and hospital stay was 30%(N = 146) and 37%(N = 182), respectively. 54% of patients with sepsis, 51% with stroke, 65% with aspiration pneumonia and 27% with heart failure died in the HDU. Predictors of in-hospital mortality were age ≥ 45 years versus 18−44 (adjusted Hazard Ratio (aHR) 1.56, 95% CI 1.07–2.28, p = 0.03), blood pressure <90mmHg (aHR 2.33, 95%CI 1.48–3.81, p < 0.001), Glasgow Coma Scale score ≤8 versus 14−15 (aHR 2.13, 95%CI 1.24–3.64, p = 0.02) and oxygen saturation at room air < 90% (aHR 1.62, 95%CI 1.04–2.51, p = 0.03). The area under the curve predicting in-hospital mortality was 0.69 (95%CI 0.65–0.73) for the NEWS- and UVA scores, 0.66 (95%CI 0.62–0.70) for the MEWS-, and 0.65 (95%CI 0.61–0.69) for the qSOFA score. Conclusion Sepsis and non-communicable diseases were the most common diagnoses. Scores predicted in-hospital mortality with a moderate accuracy.

Identifying determinants for promoting public engagement via Chinese E-government TikTok in public health emergencies

Background Social media have become vital tools for governments during public health crises, enabling the timely dissemination of critical information, health guidance, and public engagement. In China, public engagement through social media, particularly TikTok, presents unique challenges during crises. This study investigates the factors that predict public engagement via the Chinese government’s TikTok during a public health emergency, focusing on the roles of information quality, source credibility, multiple cues, immediate feedback, and trust as a mediating variable. Methods The study is grounded in the Elaboration Likelihood Model (ELM) and Media Richness Theory (MRT) to frame the relationships between these variables. Data were collected through an online questionnaire survey employing convenience sampling, involving 614 respondents aged 18–40 from Hebei Province, China. Descriptive statistical analysis was performed using SPSS, and Structural Equation Modeling (SEM) was employed to test the hypotheses. Results The findings indicate that information quality and immediate feedback are significantly positively associated with public engagement via the Chinese government’s TikTok. However, source credibility and multiple cues did not have a positive impact on public engagement. Additionally, the study reveals that trust mediates the relationships between information quality, source credibility, and immediate feedback with public engagement. Conclusion These results underscore the importance of trust in fostering public engagement and highlight the potential for enhancing government communication strategies on social media during crises. The study suggests that improving the quality of information and providing timely feedback can significantly increase public engagement through social media platforms like TikTok, particularly in the context of public health emergencies.

“...In Kiambu county, there are different things being done”: A qualitative exploration of healthcare workers’ experiences with cord care practices

Introduction Neonatal sepsis (NNS) continues to be a leading cause of neonatal mortality in low-resource settings. NNS can occur secondary to umbilical stump infections (omphalitis) and this requires preventative cord care practices. To address this, the World Health Organization (WHO) issued context-specific recommendations for the use of 7.1% chlorhexidine (CHX) solution in areas with high neonatal mortality. Despite adoption of this policy, monitoring and evaluation of this guidance is lacking. This study describes the experiences of healthcare workers (HCWs) regarding cord care practices in Kiambu County, Kenya. Methods We employed an exploratory qualitative design. Key informant interviews were conducted with 38 HCWs between October 6th and November 9th 2022 in six healthcare facilities in Kiambu County. Data were analyzed thematically using NVivo software. Results While Ministry of Health guidelines endorse WHO’s recommendation for the nation-wide use of CHX for neonatal cord care, its implementation in Kiambu county remains varied. HCWs continue to favour surgical spirit, and one of the healthcare facilities in our study adopted a facility-based policy of dry cord care. HCWs also relayed that mothers often combined modern and traditional methods. Some HCWs reported high satisfaction with CHX due to its perceived effectiveness, ease of use, and faster stump healing. However, systemic and product-specific barriers to uptake were noted. Systemic issues included frequent CHX stock-outs and high out-of-pocket costs due to inconsistent county-level supply and lack of insurance coverage. Product-specific concerns such as difficulty of application and reported adverse effects (e.g., delayed cord detachment, sepsis, burns) further impeded use. Knowledge gaps and contrary instructions among and between both the HCWs and mothers also undermined CHX use. Conclusion Despite CHX being the recommended intervention for cord care in Kenya, its inconsistent use in Kiambu county reflects gaps in policy enforcement, supply chain reliability, and health education. The persistence of outdated or non-recommended cord care practices underscores a critical lack of oversight. To realize CHX’s full public health potential, urgent policy action is needed to ensure consistent availability, enforcement of national neonatal guidelines, and investment into inclusive IEC materials. Bridging the gap between policy and practice is essential for reducing neonatal morbidity and achieving equitable progress towards Kenya’s goal of Universal Health Coverage.

On China’s image constructed from western news coverage of China’s humanitarian aid

Despite the growing body of research on media representations of China during the COVID-19 outbreak, limited scholarly attention has been devoted to Western news coverage of specific events involving China. Based on a self-built corpus of reports from English-speaking news sources on “China’s Humannitarian Aid to Europe (2019-2024)”, this study aims to contribute to the scholarship by examining how Western news media represents China’s humanitarian aid to Europe, using quantitative and qualitative methods. The study employs corpus-based Critical Metaphor Analysis to analyze how China is linguistically represented through discursive strategies. Guided by the conceptual metaphor THE NATION AS PERSON, the analysis reveals a series of secondary metaphors—including CHINA AS PERSON, EUROPE AS PERSON, and AMERICA AS PERSON. In particular, CHINA AS PERSON is associated with multifaceted images such as “a politician,” “an upstart,” “a vendor,” “an aggressor,” and “a dictator,” which collectively serve to undermine China’s efforts and reframe humanitarian aid as a diplomatic maneuver. This highlights the effectiveness of corpus-assisted CMA in in analyzing media representations of national images amid a global health crisis.

Effects of family history and sex on diabetes-related outcome in type 2 diabetes – Analysis from the tyrolean diabetes registry

Aims Family history for diabetes (FHD) is a strong risk factor for type 2 diabetes (T2D), however, little is known on its effects on the outcome. Here we aimed to analyse the effects of FHD on diabetes-related outcome. Methods 7866 patients with T2D from the Tyrolean Diabetes Registry were grouped according to their FHD status. Propensity score matching for sex, BMI, HbA1c and diabetes duration provided 1440 patients per group. Survival curves were estimated using the Kaplan-Meier plot and compared using the Log-rank test. Results Mean age at T2D diagnosis was significantly lower in the FHD group, while time to insulin initiation was independent from FHD status. FHD was associated with increased risk for neuropathy (HR 1.41 [95%CI 1.11–1.81]) but decreased risk for macrovascular disease (HR 0.84 [95%CI 0.71–0.99]). Risk for total macrovascular disease, myocardial infarction, coronary artery bypass surgery and peripheral artery disease was increased by 73–156% in males in matched groups. Conclusion Family history for diabetes is not only associated with earlier diagnosis of type 2 diabetes but also affects diabetes-related outcomes with males being more prone to cardiovascular disease and patients with FHD to increased risk for neuropathy but decreased risk for macrovascular disease.

Retraction: Clinical impact of monocyte distribution width and neutrophil-to-lymphocyte ratio for distinguishing COVID-19 and influenza from other upper respiratory tract infections: A pilot study

In Situ Coupling of MHz Acoustic Waves for the Synthesis of Hierarchical UiO-66 Metal–Organic Frameworks

Assessing food safety practices and foodborne illness risk factors in Brazilian households

Foodborne illnesses represent a pressing public health issue, with Brazilian households accounting for over a third of reported outbreaks in the country. This study aimed to investigate food handling practices in Brazilian homes, evaluating their influence on the self-reported occurrence of foodborne illnesses. A total of 1,043 respondents participated in the study. Data was collected through an online questionnaire that captured their demographic data, feeding hygiene behaviors, food storage and preparation habits. Cluster analysis identified four groups based on adherence to food safety practices. Key findings revealed significant deficiencies: only 3.07% utilize thermometer to check meat doneness, which prevents the ingestion of viable pathogens, 64.90% of participants use the same cutting board for raw meat and vegetables and only 32.70% reported washing packages before storing them in the refrigerator. Risk factor analysis highlighted that older adults and males reported fewer foodborne illness, while cluster that adhered less to proper food manipulation exhibited higher illness rates. Our findings emphasize the urgent need for targeted educational campaigns to address critical gaps, such as discouraging meat washing and promoting safe storage practices. This study underscores the importance of innovative interventions to reduce household foodborne illnesses, contributing to improved public health outcomes.

Axial Chlorine Engineering of p-Block Antimony Atomic Sites Boosts Oxygen Reduction

Co-design to consensus: Identifying the core elements of a novel intervention for pre-school children with co-occurring phonological speech sound disorder (SSD) and developmental language disorder (DLD) using a modified e-Delphi approach

Introduction Although frequently seen in clinical services, there are few interventions which have been developed specifically to meet the needs of pre-school children with co-occurring features of a phonological speech sound disorder (P-SSD) and developmental language disorder (DLD). This study aims to achieve consensus on the core elements of a novel intervention for pre-school children with co-occurring features of P-SSD and DLD (“SWanS”- Supporting Words and Sounds), where expressive vocabulary and speech comprehensibility are joint outcomes of interest. Methods Forty-seven potential core intervention elements, based on a priori findings and the wider literature, were generated by a diverse steering group of professionals and people with lived experience within a systematic co-design process. This was followed by a modified, two round, e-Delphi with expert Speech and Language Therapists (SLTs) to achieve consensus on the elements. Consensus was defined as over 75% of participants (minimum 30 SLTs) rating the elements as either appropriate or very appropriate on a Likert of 1–5, with an inter-quartile range of one or below. If consensus was not achieved in round 1, free text comments were used to generate amended statements for the second round. Results Consensus was achieved on 42/47 statements in round 1. During the revision process, one statement was discarded; six statements which did not achieve consensus were re-worded; two statements which required further clarity had examples added; four statements were merged into two statements. Consensus was reached on 8/8 statements presented in round 2, resulting in 44 final statements achieving consensus in total. Conclusions Core elements of a novel intervention have been identified through co-design with a diverse group of stakeholders followed by consensus with expert SLTs. Additional flexibility was required within some core elements in order to achieve consensus. Implications for future implementation are discussed.

Cobalt-Catalyzed Enantioconvergent Decarboxylative N-Alkylation

Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences

The prevalence of Leukaemia, a malignant blood cancer that originates from hematopoietic progenitor cells, is increasing in Southeast Asia, with a worrisome fatality rate of 54%. Predicting outcomes in the early stages is vital for improving the chances of patient recovery. The aim of this research is to enhance early-stage prediction systems in a substantial manner. Using Machine Learning and Data Science, we exploit protein sequential data from commonly altered genes including BCL2, HSP90, PARP, and RB to make predictions for Chronic Myeloid Leukaemia (CML). The methodology we implement is based on the utilisation of reliable methods for extracting features, namely Di-peptide Composition (DPC), Amino Acid Composition (AAC), and Pseudo amino acid composition (Pse-AAC). We also take into consideration the identification and handling of outliers, as well as the validation of feature selection using the Pearson Correlation Coefficient (PCA). Data augmentation guarantees a comprehensive dataset for analysis. By utilising several Machine Learning models such as Support Vector Machine (SVM), XGBoost, Random Forest (RF), K Nearest Neighbour (KNN), Decision Tree (DT), and Logistic Regression (LR), we have achieved accuracy rates ranging from 66% to 94%. These classifiers are thoroughly evaluated utilising performance criteria such as accuracy, sensitivity, specificity, F1-score, and the confusion matrix.The solution we suggest is a user-friendly online application dashboard that can be used for early detection of CML. This tool has significant implications for practitioners and may be used in healthcare institutions and hospitals.

Emulating the EPIC trial using VetCompass primary-care data: causal effects of pimobendan in UK dogs with grade IV/VI heart murmurs

Target trial emulation applies design principles from randomised controlled trials (RCTs) to the analysis of observational data, potentially replicating RCT results in real-world settings. The EPIC trial reported that pimobendan delays the onset of congestive heart failure (CHF) and extends survival in dogs with preclinical degenerative mitral valve disease (DMVD). The current study aimed to explore the extent to which target trial emulation approximates the EPIC trial results in a primary-care setting. Grade IV/VI murmur diagnosis was defined as the treatment intervention stage. There were 928 dogs ≥ 6 years and ≤ 15 kg at first grade IV/VI murmur diagnosis recorded from January 1, 2016, to December 31, 2018 in the VetCompass database included in the study. A causal inference “target trial emulation” approach using VetCompass anonymised clinical data was designed to replicate the EPIC trial with adaptation for a primary-care setting and to address immortal time bias, confounding bias and loss to follow-up. After bias adjustments to establish causal effects using observational data, the 5-year CHF cumulative incidence was lower in dogs prescribed pimobendan (34.1%, 95% CI 26.5–42.0) than dogs not prescribed pimobendan (56.3%, 95% CI 52.8–59.8). Dogs prescribed pimobendan had 311 fewer days of health lost to CHF (95% CI 224–395 days) within 5 years. Dogs prescribed pimobendan lived longer (adjusted mean survival time 1051 days, 95% CI 967–1125) than dogs not prescribed pimobendan (905 days, 95% CI 871–940 days). This study demonstrates that target trial emulation within veterinary research can replicate findings from RCTs. Clinically, the current findings suggest that preclinical grade IV murmur diagnosis may offer an appropriate intervention stage to begin pimobendan therapy in dogs with presumed DMVD.

High-Throughput Multiplexed Quantification of Molecules by Aptamer Sequencing (Apt-seq) in Single Cells

On the solutions of fractional differential equations with modified Mittag-Leffler kernel and Dirac Delta function: Analytical results and numerical simulations

In this paper we define, for the first time, the modified fractional derivative with Mittage-Leffler kernel of Riemann–Liouville (R-L) type of arbitrary order δ>0delta. We derive the infinite series representations for the modified derivatives of R-L and Caputo types and present a relationship between them. We also investigate the modified derivatives for the Dirac delta functions, and study related fractional differential equations. Explicit solutions were presented for linear fractional differential equations with constant coefficients via the Laplace transform. A fractional model with the modified derivative is considered and numerical simulations were presented.

Covalent Destabilizing Degrader of AR and AR-V7 in Androgen-Independent Prostate Cancer Cells

Exploring the spatiotemporal dynamics and resilience assessment of urban networks from the perspective of population flow

The spatial patterns of population mobility serve as a critical indicator for urban network characterization, providing an essential foundation for resilience assessment. Based on the complex network theory, this study constructs urban networks using the Baidu Migration Big Data of the Jiangsu-Zhejiang-Shanghai region in 2023 to analyze the spatiotemporal dynamics of population flow. Through a research framework of “structure measurement-scenario simulation-resilience assessment,” the study systematically reveals the response mechanisms of urban networks. The static characteristics of network resilience in the normal scenario and the dynamic characteristics in the disruption scenario were analyzed. The results are as follows: (1) Population flow is dense in the central region and sparse in the north and south. Network clusters exhibit dual characteristics of “administrative boundary constraints” and “economic gravity dominance”, forming more easily among developed cities across provinces or adjacent cities within the same province. The overall connection intensity of the network during holidays is markedly higher than that in the daily period. However, daily contact between developed cities is more frequent than that during holidays, indicating strong intercity commuting and routine movement. (2) In the normal scenario, core cities possess prominent centrality, while the hierarchy of the network is less pronounced. The agglomeration among nodes is moderate but features evident asymmetric connections. The transmission efficiency is relatively high. (3) In the disruption scenario, both the network transmission efficiency and the path connectivity experience phased changes, and the impact of deliberate disturbances on resilience is more significant than that of random disturbances. A handful of cities with crucial influence constitute the core network. This research aims to reveal the resilience characteristics and response mechanisms of population flow networks, offering insights into regional spatial coordination and sustainable development.

Polarization-Ordered Tandem in Superlattice Promoting C–N Photocatalytic Cross-Coupling

Exploration of predictive factors based on oral and intestinal bacterial flora for treating patients with urothelial carcinoma

Purpose The role of intestinal flora in carcinogenesis and chemotherapy efficacy has been increasingly studied; however, comparisons between oral and intestinal flora remain limited. Given the oral microbiome’s role in systemic inflammation and immune modulation, it may significantly influence cancer progression and response to immunotherapy. This study aimed to identify the microbial changes in urothelial carcinoma (UC) by analyzing oral saliva and stool samples from healthy individuals and UC patients. We also examined the association between microbial composition and immune checkpoint inhibitor (ICI) response. Methods A total of 20 healthy individuals and 38 patients with UC were analyzed. Among them, 27 patients with UC underwent ICI treatment. Oral saliva and stool samples were analyzed for 16S rRNA sequences to assess bacterial composition. Operational taxonomic units were generated, and phylogenetic analysis was performed using the Illumina BaseSpace. Results Patients with UC showed higher Veillonellaceae and Prevotellaceae levels in saliva and stool, with lower levels of these bacteria associated with more prolonged overall survival and progression-free survival, particularly Veillonellaceae in stool. A higher neutrophil-to-lymphocyte ratio correlated with increased levels of these bacteria. Conclusion Veillonellaceae and Prevotellaceae are potential microbial biomarkers of survival outcomes and ICI efficacy in patients with UC. Non-invasive oral microbial sampling may facilitate personalized cancer treatment strategies.

Clarifying species identity in Aphanopus using wavelet-based otolith shape analysis

Accurate species identification is crucial for effective fisheries management, particularly for cryptic species with overlapping ranges and similar morphologies. This study explores the coexistence and distribution of Aphanopus carbo and Aphanopus intermedius in the northeastern Atlantic over four decades using otolith contour analysis. Otolith samples were collected from Madeira and the African coast between 1990 and 2021 and analyzed using a wavelet-based method, which improves species discrimination by capturing finer morphological details. The analysis revealed stable species proportions over time, with A. carbo generally dominating the catches (~55–60%), except in 2010 when a decline was observed. A higher presence of A. intermedius in offshore areas may be associated with increased salinity near Madeira Island during the spawning season (October–December). Environmental changes, including variations in temperature and salinity at depths greater than 800 m, and the progressive expansion of the drifting longline fishery to new grounds, likely influenced these patterns. This study demonstrates that combining otolith contour analysis with genetically identified reference specimens enhances species discrimination and provides valuable insights into population dynamics and habitat use. These findings contribute to more effective fisheries management and stock assessments for these economically important scabbardfish in the northeastern Atlantic.