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Understanding support needs of African and African-Caribbean people living with dementia, their care partners and families, and impacts of delayed support: Identifying inclusive strategies to facilitate social care support: A study protocol

African and African-Caribbean (AAC) people living with dementia (PLWD) are a population at high risk of inequitable access to health and social care services, and have poor health and wellbeing outcomes. Research suggests they are likely to experience failings in care, be recognised by services late and at points of crisis, and are at increased risk of institutionalisation. This research aims to examine the experiences of AAC PLWD, their care partners and families of accessing social care services and support. Employing an intersectionality theoretical framework, this study will use a multi-method, flexible exploratory sequential design. An evidence synthesis using the JBI meta-aggregation approach, co-produced with AAC PLWD, their care partners, and families, will be conducted to synthesise existing evidence on the experiences of AAC PLWD, their care partners’ and families’ of support seeking in the UK. Narrative interviews will involve 3 sequential interviews conducted with 40 AAC PLWD (n = 10 per site), with opportunities for dyadic interviews with care partners and families. Ethnographic fieldwork will be conducted across 4 Local Authority sites within Adult Social Care teams (n = 30 days per site) to provide insight into institutional and organisational processes and cultures, and staff practices and engagement with PLWD. Artistic art workshops will be conducted to facilitate a diverse range of participant voices and support meaningful engagement for AAC PLWD at increased risk of isolation. Ethical approval has been obtained. Dissemination will include peer reviewed publications, conference presentations and free publicly available resources for health and social care professionals, third-sector partners, PLWD and their carer partners.

Population genomics of Nigerian goat breeds and neighbouring populations in the West Africa–Cameroon transboundary livestock corridor

Indigenous goats in Nigeria and neighbouring countries support livelihoods across forest–savanna–Sahel environments, yet genomic structure, connectivity history, and adaptive signals are rarely investigated in a single corridor-scale transboundary framework. We analysed three Nigerian populations (Sahel, Red Sokoto/Maradi, and West African Dwarf; WAD) and seven neighbouring populations from Burkina Faso, Mali, and Cameroon using 46,431 autosomal markers from 209 unrelated animals (with a South Asian outgroup where needed). We tested whether recurrent vernacular labels map onto shared genomic backgrounds across borders, reconstructed time-layered connectivity, quantified demographic contraction and inbreeding, and prioritised candidate adaptive regions using a structure-aware approach. Model-based ancestry and principal component analysis supported three transboundary genomic backgrounds: (i) a Sahel–Sudan background spanning Nigeria, Burkina Faso, and Mali; (ii) a southern Djallonké/WAD background spanning Nigeria, Burkina Faso, and Mali; and (iii) a distinct Cameroon dwarf lineage, with Guéra representing a drifted subgroup within the Sahel–Sudan background. Admixture-timing analysis, interpreted as approximate dates inferred from linkage-disequilibrium decay, suggested very recent cross-border involving Nigerian Sahel goats (~30–40 years under the assumed generation interval), superimposed on older Sahelian–dwarf exchange (~160–1,000 years). Effective population size declined from ~1,400–2,700 at ~960 generations ago to ~40–111 at 13 generations ago. Runs of homozygosity indicated low-to-moderate genomic inbreeding (0.004–0.040), with long segments (>8 Mb) most pronounced in Guéra and Red Sokoto/Maradi. A multi-statistic composite selection scan identified 53 candidate windows. Enrichment highlighted adhesion and translation quality-control themes in the Djallonké/WAD background background, neuronal/neuroendocrine terms in Guéra, and olfactory transduction in the Sahel–Sudan background. These results define transboundary genomic backgrounds rather than country-bounded “breeds” and provide background-specific hypotheses that can be validated in resilience-oriented breeding under ongoing mobility.

Image-based identification and DEA-based optimization modeling of antibiotic packaging using unsupervised learning techniques

Background Ensuring medication safety requires accurate identification of antibiotic packaging, especially within pharmacy automation and dispensing systems. Advanced imaging and machine learning offer novel avenues for physical package recognition. Objective To investigate visual and textual features of antibiotic packages and evaluate their relationship with identification outcomes using unsupervised learning and efficiency-based analysis. Methods Thirty-six antibiotic formulations from Thailand (2016–2021) were analyzed using binary imaging, entropy metrics, packaging area ratio (PAR), and optical character recognition (OCR). K-means clustering was applied to segment package groups, and data envelopment analysis (DEA) was used to assess relative efficiency without assuming predefined functional relationships between inputs and outputs. Results Nine distinct image clusters were identified. Packages with mid-range entropy (7.1–7.5) and PAR (1.2–1.45) were associated with higher identification consistency. OCR text confidence influenced identification outcomes. DEA identified clusters with relatively efficient input–output configurations. Conclusion Integrating image-derived metrics and OCR-based features supports automated antibiotic package identification. This framework provides a structured approach for evaluating packaging characteristics in pharmacy workflows.

Mission imputable: Effects of missing data processing on infectious disease detection and prognosis

Background Missing data in medical datasets poses significant challenges for developing effective AI/ML pipelines. Inaccurate imputation can lead to biased results, reduced model performance, and compromised clinical insights. Understanding how different imputation methods affect AI/ML model performance is crucial for ensuring accurate clinical findings. Objective This study systematically investigates the effects of different imputation methods on AI/ML model performance and their clinical implications. Methods We investigate the impact of six different missing data strategies on the performance of common classification algorithms for analyzing medical data. The performance was evaluated based on sensitivity and specificity metrics for the tasks of predicting COVID-19 diagnosis and patient deterioration. We also perform feature analysis to understand the clinical implications that the choice of imputation method has. Results The findings reveal that the effects of imputation depend on the clinical setting. For a general screening cohort (Einstein Data4u), multivariate imputation by chained equations (MICE) yielded the best performance in clinical settings, resulting in a 26% improvement in sensitivity compared to baseline methods and unmasking critical viral coinfections. Conversely, in an intensive care cohort (MIMIC-IV), complete-case analysis initially showed higher raw predictive metrics. However, further analysis demonstrates that this stems from selection bias driven by informative missingness (MNAR), where testing patterns are intrinsically tied to patient severity. Thus, while imputation recovers diagnostic signals in sparse screening data, it serves as a crucial tool for reducing bias in high-acuity settings. Conclusion This study demonstrates the critical impact of missing data imputation on AI/ML model performance and the resulting clinical insights. Our findings underscore the importance of selecting appropriate imputation techniques tailored to the specific characteristics of medical data to ensure accurate and reliable AI/ML predictions. By utilizing a rigorous cross-validation pipeline and a systematic comparison, we provide insights for selecting the most appropriate imputation methods for clinical decision-making applications.

Potential of Korean forest tree seed extracts as multifunctional bioresources: Evaluation of Antioxidant, anti-inflammatory, whitening, and anticancer activities

Forest tree seeds are mass produced for afforestation and forest restoration programs, but are mostly underutilized beyond propagation. Here, we aimed to evaluate the antioxidant, anti-inflammatory, anticancer, and tyrosinase-inhibitory activities of seed extracts of seven economically important forest tree species in the Republic of Korea to explore their potential as multifunctional natural bioresources. The seed extracts of Alnus japonica , Chamaecyparis obtusa , Cornus kousa , Phellodendron amurense , Pinus densiflora , Prunus sargentii , and Quercus glauca were comparatively assessed using multiple in vitro assays. The results revealed clear species-dependent functional profiles rather than uniform bioactivities across species. Q. glauca exhibited strong antioxidant activity along with significant anti-inflammatory and tyrosinase-inhibitory activities under the present screening conditions, while C. obtusa presented considerable anticancer activity against several cancer cell lines. A. japonica exhibited the highest tyrosinase-inhibitory activity, followed by Q. glauca and C. obtusa ; A. japonica extract also showed a strong antioxidant capacity. Overall, the results revealed clear species-dependent differences in bioactivity profiles among the seven seed extracts under the present screening conditions, providing a comparative baseline for further compound-level and mechanistic studies. By focusing on seed resources generated within existing afforestation systems, we highlight a sustainable approach to valorize forest-derived by-products without additional pressure on natural ecosystems. As all assays were performed at single fixed concentrations using crude extracts, the present findings should be interpreted as a comparative screening; dose–response characterization, selectivity profiling, and identification of active compounds and their mechanisms of action are required next steps.

Retraction: Hybrid deep learning and feature selection approach for autism detection from rs-fMRI data

Risk factors for decline in estimated glomerular filtration rate amongst Malawian adults living in rural Karonga: Protocol for a prospective cohort study using cystatin C- and creatinine-based eGFR

Background The global burden of chronic kidney disease (CKD) is rising, disproportionately impacting on low- and middle-income countries. In African populations, use of serum creatinine to estimate glomerular filtration rate (GFR) significantly underestimates CKD prevalence, contributing to its under-recognition as a health problem. Serum cystatin C provides more accurate estimates of Iohexol measured GFR than creatinine. Early diagnosis and treatment of CKD is essential to reduce premature morbidity and mortality. However, little is known about risk factors for CKD development and progression in Africa owing to limited longitudinal data. This study aims to determine risk factors for progressive kidney function decline among adults living in rural, northern Malawi. Methods This protocol describes a prospective study being conducted in a general population Health and Demographic Surveillance Site in rural Karonga, Malawi (2024–2025). Eligible participants are adults aged 18 years and over who participated in two population-based surveys of long-term health conditions (2013–2016 and 2021–2025), with availability of baseline measures. New household-level data is being prospectively collected on CKD risk factors, alongside blood and urine samples. Cystatin C and creatinine will be tested on individual-level paired, stored serum samples collected at three longitudinal time points. Urine will undergo dipstick urinalysis, microscopy and testing for albumin and creatinine to quantify proteinuria. The primary outcome will be sustained 25% reduction in estimated GFR (eGFR) from baseline and change in eGFR category, determined using serum cystatin C. Multivariable logistic regression will be used to determine effect size estimates of key risk factors for kidney function decline. Discussion This study will provide important data on risk factors for eGFR decline and CKD progression amongst Malawian adults. The findings will inform future research into important context-specific risk factors, and could directly inform health policies in Malawi for targeting CKD screening, prevention and treatment strategies to high-risk patient groups.

Implementation and acceptability of high efficiency particulate air filters to reduce respiratory infections in care homes: Process evaluation of the AFRI-c cluster randomised controlled trial

Respiratory infections are easily transmitted within care homes. Within a clinical trial (called AFRI-c), which tested the effectiveness of high-efficiency-particulate-air (HEPA) filters to reduce respiratory infections in care home residents, we conducted a mixed-methods process evaluation. We aimed to understand their acceptability, fidelity, and implementation to aid interpretation of the effectiveness findings. We used qualitative remote and face-to-face interviews with staff (n = 25), residents (n = 20), and relatives (n = 12) from care homes (n = 22) in the AFRI-c trial. We purposively sampled homes for variation in size, nursing or residential provision, and deprivation. We used reflexive thematic analysis, drawing on normalisation process theory to understand implementation. We used staff questionnaires (n ranges from 191 to 351 depending on questionnaire) and resident (n = 1158) questionnaires with descriptive and regression analyses. We used the triangulation protocol to integrate qualitative and quantitative findings. The use of HEPA filters became normalised, although some residents disliked the draught. Self-reported intervention fidelity was high, which is important context for interpreting the trial’s null outcome for infection reduction. HEPA filters made no difference to resident or staff satisfaction with the care home environment. We found no evidence that using HEPA filters changed infection control and prevention practices. While staff felt it was a priority to prevent respiratory infections, residents were more concerned about quality of life and care. Our mixed methods process evaluation of the AFRI-c trial found the use of HEPA filters was acceptable, with high levels of adherence and low levels of contamination, suggesting that the null trial results were not due to poor adherence. Some effort was required to ensure they were kept on. Approaches to data collection may have caused under-reporting of mild infections. We should not assume that infection prevention is always a priority for residents.

Selective GPR17 antagonism enhances structural and functional recovery in animal models of demyelination

Myelination, driven by differentiation of oligodendrocyte precursor cells, is critical for metabolic and structural support and efficient axonal signal transmission in neurons. Loss of myelin is a hallmark of multiple sclerosis and other devastating demyelinating disorders. As demyelination persists, neurons become increasingly vulnerable, leading to neurodegeneration and chronic disability. Restoring myelin through endogenous repair mechanisms offers a promising therapeutic approach to mitigate progressive neuronal loss. One key regulator of myelination is the G protein-coupled receptor 17, GPR17, whose chronic upregulation in oligodendrocyte precursor cells is commonly seen with myelin injury. In line with single-nucleus transcriptomic data showing predominant expression of GPR17 in committed oligodendrocyte precursor cells, our postmortem immunohistochemical analyses of MS patient tissue revealed a significant upregulation of GPR17 + /BCAS1 + oligodendrocyte precursor cells adjacent to and in demyelinated lesions. Importantly, remyelinated lesions lacked GPR17 immunoreactivity, consistent with a model in which sustained GPR17 expression is associated with demyelination and impaired oligodendrocyte precursor cell differentiation. To test the impact of pharmacological GPR17 inhibition on remyelination, we evaluated the effects of a novel, selective GPR17 antagonist in cuprizone-induced murine demyelination models. This toxin-induced approach has been widely used to study mechanisms of de- and remyelination, in the absence of the full inflammatory complexity of demyelinating diseases such as multiple sclerosis. We show that oral treatment results in robust functional recovery consistent with remyelination, as evidenced by improved spatial memory and recovery of visual evoked potential latency delays. GPR17 antagonism also accelerated structural remyelination in the corpus callosum and optic nerve. Together, these findings support a role for pharmacological GPR17 antagonism in promoting remyelination and highlight this G protein-coupled receptor as a promising therapeutic target for demyelinating disorders.

Dual-input deep learning system for microbial identification from blood agar plates

Objective The morphological classification of microbial cultures and colonies requires specialized knowledge and experience; therefore, automation in this field remains limited. This study examines the feasibility of automating the identification of pathogenic microbial species from colony images of cultured microorganisms. Methods Two distinct image datasets were constructed for 10 clinically relevant species: a colony image dataset, consisting of individual colony images cropped, and a tile image dataset, generated by dividing entire images of the culture plate into tiles. Separate ResNet-50–based models were trained in each dataset, and their outputs were integrated to evaluate the classification performance. Training was conducted using 10,048 colony images and 23,003 tile images collected from 418 strains, and performance was assessed with five-fold cross-validation (K = 5). Results The colony image model achieved a sensitivity of 0.934 and a specificity of 0.993, while the tile image model achieved a sensitivity of 0.918 and a specificity of 0.991. Integration of the two models into an ensemble model further improved performance, yielding a sensitivity of 0.955 and a specificity of 0.995 when tested on 76 independent strains. Conclusion The ensemble model approach provides high accuracy and robustness, suggesting its potential technical contributions to microbial identification in clinical microbiology laboratories.

Expression of Concern: Signatures of tumor microenvironment-related genes and long noncoding RNAs predict poor prognosis in osteosarcoma

Research on Benggang identification and deformation monitoring based on optical and Radar data

Benggang erosion, a severe soil erosion landform in southern China, threatens ecological security and regional development. Conventional methods can delineate surface morphology but lack capability to characterize vertical dynamics. To address it, this study presents an integrated method of deep learning and multi-temporal InSAR based on optical and radar data, and conducts benggang identification and surface deformation monitoring in typical benggang regions of Wuhua County, Guangdong, China. The results show that a U-Net model trained on Gaofen-2 high-resolution imagery achieved accurate automated Benggang delineation with a mean IoU of 86%. Subsequently, 90 Sentinel-1 SAR scenes acquired between 2022 and 2024 were processed using SBAS-InSAR and D-InSAR techniques, with Kriging interpolation employed to generate a spatially continuous deformation field. The framework successfully resolved millimeter-scale interannual vertical surface deformation, with internal cross-validation metrics (R 2  = 0.978, RMSE = 0.544 mm, MAE = 0.313 mm) confirming high spatial consistency of the fused deformation field. The proposed framework offers a reliable and scalable technical pathway for stereoscopic Benggang monitoring and demonstrates strong potential for geohazard early-warning and ecological risk management.

Incidence of tuberculosis in patients with immune-mediated diseases undergone biological therapy: A 10-year observational study in a high-burden region of northeastern Brazil

Biological therapies are critical in the management of Immune-Mediated Diseases (IMDs), yet their use is linked to adverse effects, including an elevated risk of Tuberculosis (TB), a significant health issue in endemic regions like Brazil. This study aimed to assess the incidence of TB among patients with IMDs in rheumatology, gastroenterology, and dermatology who received biological therapy in Pernambuco between 2013 and 2023. A total of 8,623 patients were analyzed, with 90.7% diagnosed with rheumatologic diseases, 5.6% with gastroenterological diseases, and 3.7% with psoriasis. In the descriptive analysis, frequency distributions and medians with interquartile ranges were reported, and Relative Risk (RR) was calculated to compare tuberculosis occurrence across IMD groups. The overall TB incidence was 2.1%, highest in gastroenterological patients (4.7%) compared to rheumatologic (2.0%) and psoriasis patients (1.3%) with a risk ratio (RR) of 2.4 for the group of gastroenterological diseases (p < 0.001). No significant differences in TB frequency were detected before (1.16%) or during (0.97%) the use of biological therapy, nor among drug classes, except for a slightly higher incidence with infliximab (2.6%). The absence of differences between the other anti-Tumor Necrosis Factor (anti-TNF) drugs and other cytokine inhibitors suggests that TB incidence might be influenced by environmental exposure in endemic regions. Pulmonary TB was the predominant form (80.8%), and pleural TB emerged as the most frequent extrapulmonary manifestation. The results underscore the need for extensive regional studies to elucidate TB risks in IMD patients and refine treatment guidelines for immunobiological therapies.

Evaluating the cultural alignment of multilingual LLMs in typical Japanese workplace scenarios

While current evaluations of LLM cultural alignment predominantly rely on static benchmarks in Western contexts, their ability to navigate generative, high-context socio-pragmatic demands in non-Western environments remains critically underexplored. This study investigates how multilingual LLMs adapt to the Japanese workplace—a stringent stress-test environment characterized by strong high-context communication norms and rigid honorific conventions—using Hofstede’s six cultural dimensions as a heuristic framework. We evaluated five state-of-the-art LLMs (LLM-jp, Phi, Llama, Qwen, and GLM) through a large-scale crowdsourced human evaluation. Based on 1,718 valid evaluator sessions, native Japanese raters assessed model outputs to generate a holistic Japanese Workplace Cultural Alignment Score (JWCAS). To dissect the underlying communicative strategies, we paired this with a three-layer diagnostic sub-score analysis (Linguistic Form, Socio-Cultural Values, and Social Action). Our results reveal that leading multilingual models (Phi and GLM) achieved overall JWCAS scores comparable to, or significantly higher than, the native Japanese model (LLM-jp). Crucially, our sub-score analysis demonstrates that holistic evaluation metrics can obscure deep pragmatic deficits: while LLM-jp overfits to surface-level linguistic politeness (Layer 1), it shows critical weaknesses in socio-cultural values (Layer 2) and context-aware social strategies (Layer 3). In contrast, leading multilingual models demonstrate balanced competence across all layers. These findings suggest that true cultural competence requires moving beyond native linguistic mastery, highlighting the necessity of multi-dimensional diagnostic frameworks for cross-cultural AI alignment.

PAP_NER: A large-scale vietnamese administrative named entity recognition corpus and hybrid deep learning architecture

Named Entity Recognition (NER) is fundamental for automating administrative document processing in digital government systems. However, Vietnamese NLP research faces a critical infrastructure gap: existing datasets focus on generic information extraction (news, medical) rather than domain-specific administrative text. We present PAP_NER, the first large-scale, gold-standard Vietnamese administrative NER corpus comprising 162,801 sentences with 205,807 entity annotations across five entity types critical for e-Government workflows: Agency (CQ), Legal Document (VBPL), Object (ĐT), Datetime (NG), and Quantity (SL). The dataset was constructed through a rigorous human-in-the-loop annotation pipeline, achieving an inter-annotator agreement of κ = 0.85. We demonstrate PAP_NER’s value through comprehensive benchmarking of an established hybrid deep learning architecture, PhoBERT-CRF, which couples monolingual Transformer embeddings (PhoBERT) with Conditional Random Fields for structured prediction. PhoBERT-CRF achieves 97.95% Micro F1-score on the PAP_NER test set, significantly outperforming established baselines: BiLSTM+CRF (+2.01%), multilingual XLM-RoBERTa (+2.52%), and pure Transformer approaches (+0.44%). Ablation analysis reveals that the CRF layer provides statistically significant improvements for structurally complex entities (VBPL: + 0.96%, p  < 0.05, McNemar’s test). We release PAP_NER publicly (DOI: 10.5281/zenodo.18044019 ) under Creative Commons BY 4.0 license to support reproducibility and enable further research in Vietnamese administrative NLP. This work establishes a foundational dataset and methodology for addressing the Vietnamese government NER gap, with implications for low-resource language NLP research.

Cyclodextrin inclusion complexes enhance the solubility and anti-virulence activity of metronidazole against uropathogenic Proteus mirabilis

Proteus mirabilis is a major uropathogen implicated in catheter-associated urinary tract infections and infection-induced urolithiasis, often exhibiting multidrug resistance. Improving the solubility and bioactivity of existing drugs represents a promising pharmaceutical strategy to overcome these challenges. In this study, inclusion complexes of metronidazole with α-cyclodextrin (α-CD), β-cyclodextrin (β-CD), and hydroxypropyl-β-cyclodextrin (HP-β-CD) were prepared using physical mixing and kneading methods. Complexes were characterized using differential scanning calorimetry (DSC) and Fourier-transform infrared spectroscopy (FTIR). In vitro release behaviour, minimum inhibitory concentration (MIC), and effects at sub-MIC levels on motility, urease-associated phenotype, and biofilm formation were evaluated. β-CD and HP-β-CD systems demonstrated improved release profiles compared to metronidazole alone. While MIC values remained within the mg/mL range, HP-β-CD complexes showed reduced MIC relative to aqueous metronidazole, achieving a four-fold reduction in MIC. At sub-MIC concentrations, β-CD and HP-β-CD formulations were associated with significant suppression of motility, reduced urease-associated phenotypes, and inhibition of biofilm formation. Molecular docking suggested spatial compatibility between metronidazole and cyclodextrins, though mechanistic conclusions remain predictive. These findings indicate that cyclodextrin-based complexation enhances physicochemical performance of metronidazole and may support adjunctive anti-virulence modulation in P. mirabilis. Further mechanistic and in-vivo studies are required to validate translational relevance.

Helminth-derived stefin-1 selectively reduces leukemic cell viability and promotes apoptosis in U937 cells

Leukemia remains a challenging hematological malignancy, particularly due to treatment resistance and the limited availability of therapies that selectively target cancer cells. Helminth-derived cysteine protease inhibitors (stefins) are mainly known for their roles in host–parasite interactions, but their effects on cancer cells have not been well explored. In this study, we investigated the biological activity of recombinant Fasciola gigantica Stefin-1 (rFgStefin-1) in the human U937 leukemic cell line. Treatment with rFgStefin-1 reduced cell viability in a dose- and time-dependent manner, with an IC₅₀ of approximately 11 µM at 48 h. In contrast, normal peripheral blood mononuclear cells (PBMCs) showed minimal changes under the same conditions. Flow cytometry analysis demonstrated increased apoptotic cell populations together with mitochondrial membrane depolarization following rFgStefin-1 treatment. At the molecular level, rFgStefin-1 treatment was associated with an increased BAX/BCL-2 ratio, increased cleaved caspase-3 expression, reduced AKT-associated signaling, and enhanced PARP cleavage. These findings were supported by both qRT-PCR and Western blot analyses. Overall, these results suggest that rFgStefin-1 promotes apoptosis in leukemic cells while exerting limited effects on normal cells under the tested conditions. Further studies are required to clarify the underlying molecular mechanisms and to evaluate the effects of rFgStefin-1 in additional leukemia models and in vivo systems.

Outcomes of proximal humeral chondroblastoma treated with intralesional curettage, electrocauterization of the cavity and bone grafting: A retrospective study

Objectives This study aimed to evaluate the 7-year outcomes of proximal humeral chondroblastoma treated with intralesional curettage, electrocauterization of the cavity and bone grafting in pediatric patients. Methods A total of 17 patients diagnosed with proximal humeral chondroblastoma who underwent surgical treatment consisting of intralesional curettage, cavity electrocauterization, and bone grafting at our center between January 1, 2008, and January 1, 2018, were retrospectively enrolled. All patients received postoperative splint immobilization for four weeks and were followed up for a minimum of seven years. Tumor recurrence was evaluated based on clinical manifestations and radiographic X-ray examination. Clinical outcomes included functional performance assessed using the Musculoskeletal Tumor Society (MSTS) score and the Quick Disabilities of the Arm, Shoulder and Hand (Quick DASH) scale, as well as postoperative complications. Results In total, 17 patients with proximal humeral chondroblastoma were enrolled in our study, including 10 males and 7 females, with a mean age of 10.9 ± 1.8 years old (range from 8 to 14 years). Among these 17 cases, there were 6 cases with lesions extending through the epiphyseal plate, and 11 cases with lesions located inside the epiphysis. During follow-up, 1 patient suffered reoperation due to tumor recurrence. Shortening of the affected upper limb occurred in 9 patients (52.9%), which was significantly associated with a young age(P = 0.012). The mean shortening was 1.2 ± 0.9 cm compared with the contralateral limb. The overall mean postoperative MSTS was 27.5 ± 1.4 points, with no significant difference between observed the limb shortening group (27.3 ± 1.4) and the non-shortening group (27.8 ± 1.4,P = 0.550). The overall Quick DASH score averaged 4.8 ± 3.9 (0–15.9), and no statistically significant difference was detected between the shortening group (5.3 ± 4.5) and the non-shortening group (4.3 ± 2.8; P = 0.452). Conclusions Intralesional curettage, cavity electrocauterization and bone grafting is an optional treatment for children with proximal humeral chondroblastoma but still has a risk of recurrence. Shortening of the affected upper limb is a common complication, which is possibly associated with young age but has no impact on the final functional outcomes. Level of Evidence Level IV, retrospective case series.

SPP1 promotes cisplatin resistance in cervical cancer by regulating KRAS expression

Background Chemotherapy resistance is a major treatment issue for cervical cancer (CC), contributing to high morbidity and mortality. This study identifies key molecular drivers of CC progression and cisplatin (DDP) resistance, focusing on secreted phosphoprotein 1 (SPP1). Methods Differentially expressed genes (DEGs) were identified from TCGA-CC and GSE46857 datasets. Protein-protein interaction network analysis and topological ranking determined candidate genes. SPP1 expression was evaluated in CC cell lines using qPCR and Western blotting. Cisplatin-resistant CC cells were used to examine SPP1’s role in drug resistance. Functional assays assessed cell proliferation, apoptosis, migration, and invasion. Results Bioinformatics analysis identified SPP1 as a hub gene upregulated in CC. SPP1 expression was higher in CC and DDP-resistant cells. Silencing SPP1 inhibited cell growth and enhanced apoptosis, while overexpression had the opposite effects. Silencing SPP1 enhanced DDP sensitivity, while overexpression reduced DDP cytotoxicity. Mechanistically, SPP1 positively correlated with KRAS expression, and KRAS modulation partially reversed SPP1’s effects on DDP resistance. Conclusion SPP1 is upregulated in CC and DDP-resistant cells, promoting tumor progression and chemoresistance via KRAS. Targeting the SPP1-KRAS axis may help overcome chemotherapy resistance in CC.

Socioeconomic disparities in children’s neurodevelopment before and during primary education: Evidence from Bagamoyo, Tanzania

Background Socioeconomic disparities in children’s neurodevelopment constitute a critical yet understudied developmental challenge in low- and middle-income countries (LMICs), yet longitudinal evidence from Sub-Saharan Africa (SSA) remains limited. This study aimed to evaluate the relationship between household wealth and neurocognitive trajectories in a cohort of Tanzanian children aged 6 months to 10 years. Methods We randomly selected 590 children aged 6 months to 10 years in Bagamoyo district, a low to moderate-malaria-endemic area in Tanzania. Participants completed comprehensive, age-appropriate neurodevelopmental assessments. The family’s socioeconomic status was derived from their household assets. Standardized, age-adjusted neurodevelopmental scores were regressed on socioeconomic status tertiles and by developmental periods: early childhood (6 months-3 years), preschool (4–5 years), and school age (6–10 years). Results While children from high- and low-Socioeconomic status (SES) families start with similar neurodevelopmental scores, a significant and sustained gap begins to emerge around age 2 years. This divergence results in a substantial disadvantage for children from the poorest households, with scores showing a difference of −0.36 Standard Deviation (SD) (95% CI: −0.64 to −0.08) during the preschool years and −0.41 SD (95% CI: −0.73 to −0.09) at school age. Conclusion Our results suggest that programs targeting the preschool period may be most effective in mitigating disparities in neurodevelopment.