Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. Existing methods, including advanced deep learning techniques, struggle to reliably impute gene expressions due to a lack of mechanisms that explicitly consider the underlying biological knowledge of the system. In reality, it has long been recognized that gene-gene interactions may serve as reflective indicators of underlying biology processes, presenting discriminative signatures of the cells. A genomic data analysis framework that is capable of leveraging the underlying gene-gene interactions is thus highly desirable and could allow for more reliable identification of distinctive patterns of the genomic data through extraction and integration of intricate biological characteristics of the genomic data. Here we tackle the problem in two steps to exploit the gene-gene interactions of the system. We first reposition the genes into a 2D grid such that their spatial configuration reflects their interactive relationships. To alleviate the need for labeled ground truth gene expression datasets, a self-supervised 2D convolutional neural network is employed to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.

Migration experiences and reported commercial and non-commercial sexual behaviors among newly diagnosed HIV infections in China: a cross-sectional study.

BACKGROUND: Migration is known to influence human health. China has a high migration rate and a significant number of people who are HIV-positive, but little is known about how these factors intersect in sexual risk behaviors. OBJECTIVE: This study aimed to explore sexual risk behaviors between migrants and non-migrants among newly diagnosed HIV infections, and assess the changes of sexual risk behaviors with length of stay in the current city of migrants. METHODS: A cross-sectional questionnaire was conducted among people newly diagnosed with HIV from July 2018 to December 2020 who lived in Zhejiang Province. In the study, sexual risk behaviors included having multiple sexual partners and unprotected sexual behaviors (in commercial sexual behaviors, non-commercial sexual behaviors, heterosexual behaviors, and homosexual behaviors). Binary logistic regression models were employed to explore the influencing factors of sexual risk behaviors, measured by multiple sexual partners and unprotected sexual partners. RESULTS: A total of 836 people newly diagnosed with HIV/AIDS were incorporated in the study and 65.31% (546) were migrants. The percentages of non-commercial sexual behaviors among migrants were statistically higher than those of non-migrants. Commercial heterosexual behavior was higher among non-migrants compared with migrants. The proportion of study participants having unprotected sexual behaviors and multiple sexual partners with commercial/non-commercial partners was both higher among migrants compared with non-migrants. Among migrants, the likelihood of sexual risk behaviors in both commercial and non-commercial sex increased in the first 3 years and reduced after 10 years. Compared with non-migrants, migrants were statistically associated with multiple sexual partners [P = .007, odds ratio (OR) = 1.942]. However, migrants did not exhibit a significant difference in unprotected sexual behaviors compared with non-migrants. In addition, migrants aged between 18 and 45 years who relocated to the current city in the past 2-3 years tended to have multiple sexual partners (P < .05). CONCLUSIONS: People newly diagnosed with HIV engaged in different sexual risk behaviors among migrants and non-migrants and more attention should be paid to migrants. For non-migrants, it is urgent to promote the prevention of commercial sexual behaviors. For migrants, prevention of non-commercial sexual behaviors and universal access to health care especially for new arrivals who migrated to the current city for 2-3 years are needed. Moreover, sexual health education and early HIV diagnosis are necessary for the entire population.

p-Cresol Sulfate Is a Sensitive Urinary Marker of Fecal Microbiota Transplantation and Antibiotics Treatments in Human Patients and Mouse Models.

Fecal microbiota transplantation (FMT) has emerged as a highly effective therapy for recurrent Clostridioides difficile infection (rCDI) and also a potential therapy for other diseases associated with dysbiotic gut microbiota. Monitoring metabolic changes in biofluids and excreta is a noninvasive approach to identify the biomarkers of microbial recolonization and to understand the metabolic influences of FMT on the host. In this study, the pre-FMT and post FMT urine samples from 11 rCDI patients were compared through metabolomic analyses for FMT-induced metabolic changes. The results showed that p-cresol sulfate in urine, a microbial metabolite of tyrosine, was rapidly elevated by FMT and much more responsive than other microbial metabolites of aromatic amino acids (AAAs). Because patients were treated with vancomycin prior to FMT, the influence of vancomycin on the microbial metabolism of AAAs was examined in a mouse feeding trial, in which the decreases in p-cresol sulfate, phenylacetylglycine, and indoxyl sulfate in urine were accompanied with significant increases in their AAA precursors in feces. The inhibitory effects of antibiotics and the recovering effects of FMT on the microbial metabolism of AAAs were further validated in a mouse model of FMT. Overall, urinary p-cresol sulfate may function as a sensitive and convenient therapeutic indicator on the effectiveness of antibiotics and FMT for the desired manipulation of gut microbiota in human patients.

Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT.

The volume of pelvic hematoma at CT has been shown to be the strongest independent predictor of major arterial injury requiring angioembolization in trauma victims with pelvic fractures, and also correlates with transfusion requirement and mortality. Measurement of pelvic hematomas (unopacified extraperitoneal blood accumulated from time of injury) using semi-automated seeded region growing is time-consuming and requires trained experts, precluding routine measurement at the point of care. Pelvic hematomas are markedly variable in shape and location, have irregular ill-defined margins, have low contrast with respect to viscera and muscle, and reside within anatomically distorted pelvises. Furthermore, pelvic hematomas occupy a small proportion of the entire volume of a chest, abdomen, and pelvis (C/A/P) trauma CT. The challenges are many, and no automated methods for segmentation and volumetric analysis have been described to date. Traditional approaches using fully convolutional networks result in coarse segmentations and class imbalance with suboptimal convergence. In this study, we implement a modified coarse-to-fine deep learning approach-the Recurrent Saliency Transformation Network (RSTN) for pelvic hematoma volume segmentation. RSTN previously yielded excellent results in pancreas segmentation, where low contrast with adjacent structures, small target volume, variable location, and fine contours are also problematic. We have curated a unique single-institution corpus of 253 C/A/P admission trauma CT studies in patients with bleeding pelvic fractures with manually labeled pelvic hematomas. We hypothesized that RSTN would result in sufficiently high Dice similarity coefficients to facilitate accurate and objective volumetric measurements for outcome prediction (arterial injury requiring angioembolization). Cases were separated into five combinations of training and test sets in an 80/20 split and fivefold cross-validation was performed. Dice scores in the test set were 0.71 (SD ± 0.10) using RSTN, compared to 0.49 (SD ± 0.16) using a baseline Deep Learning Tool Kit (DLTK) reference 3D U-Net architecture. Mean inference segmentation time for RSTN was 0.90 min (± 0.26). Pearson correlation between predicted and manual labels was 0.95 with p < 0.0001. Measurement bias was within 10 mL. AUC of hematoma volumes for predicting need for angioembolization was 0.81 (predicted) versus 0.80 (manual). Qualitatively, predicted labels closely followed hematoma contours and avoided muscle and displaced viscera. Further work will involve validation using a federated dataset and incorporation into a predictive model using multiple segmented features.

Equol Induces Gonadal Intersex in Japanese Medaka (Oryzias latipes) at Environmentally Relevant Concentrations: Comparison with 17ß-Estradiol.

Equol is present in the aquatic environment via livestock waste and runoff discharge; however, it remains unclear whether it can induce gonadal intersex in fish at environmentally relevant concentrations. This study evaluated adverse effects of equol on gonadal development by exposing transgenic Japanese medaka (Oryzias latipes) from hatching for 100 days. Equol induced intersex incidence in male medaka in a dose-dependent manner, and the benchmark dose corresponding to 10% intersex incidence (BMD10) was 11.5 ng/L (95% confidence interval (CI): 5.8 ng/L, 19.8 ng/L), which was comparable to the required dose of 17ß-estradiol (E2ß) (9.0 ng/L, 95% CI: 6.6 ng/L, 11.0 ng/L). Equol exposure resulted in reduced plasma 11-ketotestosterone (11-KT) concentrations in male medaka at 1.3 ng/L, while reduced plasma 11-KT concentrations were observed at a relatively high concentration (6.4 ng/L) of E2ß. Such antiandrogenic property could partly explain the comparable potency of equol with that of E2ß to induce intersex at relatively low concentrations, although the binding affinity of equol to medaka estrogen receptor α (EC50 939.4 nM) was 230-fold lower than that (4.07 nM) of E2ß. This study for the first time demonstrated that equol could induce intersex in medaka fish at environmentally relevant concentrations.

Occurrence and Maternal Transfer of Chlorinated Bisphenol A and Nonylphenol in Pregnant Women and Their Matching Embryos.

Prenatal exposure has recently raised concerns over the health risks of endocrine disruptors; however, little is known about their extent and the mechanisms of maternal transfer in the embryo stage. In this study, bisphenol A (BPA), nonylphenol (NP), and their six chlorinated derivatives were quantified in decidua samples from 25 pregnant women and their matching embryos, which were collected as chorionic villi samples. Monochloro-BPA (MCBPA), dichloro-BPA (DCBPA), monochloro-NP (MCNP), and dichloro-NP (DCNP) were detected in over 70% of the decidua or chorionic villi samples, while BPA, NP, trichloro-BPA (TCBPA), and tetrachloro-BPA (TeCBPA) were detected in less than half. The geometric mean (GM) concentrations of MCBPA, DCBPA, NP, MCNP, and DCNP in chorionic villi samples were 0.13, 0.17, 5.33, 4.52, and 2.44 ng/g dw, respectively, higher than those in maternal decidua samples, which were 0.10, 0.12, 3.27, 1.85, and 0.74 ng/g dw, respectively, while the GM concentration of BPA was lower in chorionic villi samples (0.09 ng/g dw) than in maternal decidua (0.10 ng/g dw). The ratios of the average lipid-normalized concentrations of chemicals in chorionic villi to those in maternal decidua (EMR) were calculated to be 1.53 for MCNP and 2.38 for DCNP, while those of BPA, MCBPA, DCBPA, and NP were lower than 1 (0.39-0.97). Such obvious difference in maternal transfer is probably due to their different affinities to plasma proteins, as exemplified by the correlation between EMR and the binding affinities to T4 transport proteins (TTR). This is the first report on the occurrence and maternal transfer of chlorinated derivatives of BPA and NP in human embryos and decidua.

Ubiquitous occurrence of chlorinated byproducts of bisphenol A and nonylphenol in bleached food contacting papers and their implications for human exposure.

The occurrence of bisphenol A (BPA), nonylphenol (NP), and their six chlorinated byproducts were investigated in 74 food contacting papers (FCPs) from China, the U.S.A., Japan, and Europe using a sensitive dansylation LC-MS/MS method. BPA (

Religion, Geography, and Risky Sexual Behaviors Among International Immigrants Living in China: Cross-Sectional Study.

Background: Behavioral differences exist between countries, regions, and religions. With rapid development in recent decades, an increasing number of international immigrants from different regions with different religions have settled in China. The degrees to which sexual behaviors-particularly risky sexual behaviors-differ by religion and geographical areas are not known. Objective: We aim to estimate the associations of religion and geographical areas with sexual behaviors of international immigrants and provide evidence for promoting the sexual health of international immigrants. Methods: A cross-sectional study was conducted via the internet with a snowball sampling method among international immigrants in China. In our study, risky sexual behaviors included having multiple sexual partners and engaging in unprotected sex. Descriptive analysis was used to analyze the basic characteristics of international immigrants as well as their sexual behaviors, religious affiliations, and geographical regions of origin. Multivariate binary logistic regression analyses with multiplicative and additive interactions were used to identify aspects of religion and geography that were associated with risky sexual behaviors among international immigrants. Results: A total of 1433 international immigrants were included in the study. South Americans and nonreligious immigrants were more likely to engage in risky sexual behaviors, and Asian and Buddhist immigrants were less likely to engage in risky sexual behaviors. The majority of the Muslims had sexually transmitted infection and HIV testing experiences; however, Muslims had a low willingness to do these tests in the future. The multivariate analysis showed that Muslim (adjusted odds ratio [AOR] 0.453, 95% CI 0.228-0.897), Hindu (AOR 0.280, 95% CI 0.082-0.961), and Buddhist (AOR 0.097, 95% CI 0.012-0.811) immigrants were less likely to report engaging in unprotected sexual behaviors. Buddhist immigrants (AOR 0.292, 95% CI 0.086-0.990) were also less likely to have multiple sexual partners. With regard to geography, compared to Asians, South Americans (AOR 2.642, 95% CI 1.034-6.755), Europeans (AOR 2.310, 95% CI 1.022-5.221), and North Africans (AOR 3.524, 95% CI 1.104-11.248) had a higher probability of having multiple sexual partners. Conclusions: The rates of risky sexual behaviors among international immigrants living in China differed depending on their religions and geographical areas of origin. South Americans and nonreligious immigrants were more likely to engage in risky sexual behaviors. It is necessary to promote measures, including HIV self-testing, pre-exposure prophylaxis implementation, and targeted sexual health education, among international immigrants in China.

Consistency-guided Differential Decoding for Enhancing Semi-supervised Medical Image Segmentation.

Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data, especially on volumetric medical image segmentation. Unlike previous SSL methods which focus on exploring highly confident pseudo-labels or developing consistency regularization schemes, our empirical findings suggest that differential decoder features emerge naturally when two decoders strive to generate consistent predictions. Based on the observation, we first analyze the treasure of discrepancy in learning towards consistency, under both pseudo-labeling and consistency regularization settings, and subsequently propose a novel SSL method called LeFeD, which learns the feature-level discrepancies obtained from two decoders, by feeding such information as feedback signals to the encoder. The core design of LeFeD is to enlarge the discrepancies by training differential decoders, and then learn from the differential features iteratively. We evaluate LeFeD against eight state-of-the-art (SOTA) methods on three public datasets. Experiments show LeFeD surpasses competitors without any bells and whistles, such as uncertainty estimation and strong constraints, as well as setting a new state of the art for semi-supervised medical image segmentation. Code has been released at https://github.com/maxwell0027/LeFeD.

MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images.

Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that provides 3-4 times higher resolution than traditional ultrasound, potentially enabling low-cost, accurate diagnosis of prostate cancer. Accurate prostate segmentation is crucial for prostate volume measurement, cancer diagnosis, prostate biopsy, and treatment planning. However, prostate segmentation on micro-US is challenging due to artifacts and indistinct borders between the prostate, bladder, and urethra in the midline. This paper presents MicroSegNet, a multi-scale annotation-guided transformer UNet model designed specifically to tackle these challenges. During the training process, MicroSegNet focuses more on regions that are hard to segment (hard regions), characterized by discrepancies between expert and non-expert annotations. We achieve this by proposing an annotation-guided binary cross entropy (AG-BCE) loss that assigns a larger weight to prediction errors in hard regions and a lower weight to prediction errors in easy regions. The AG-BCE loss was seamlessly integrated into the training process through the utilization of multi-scale deep supervision, enabling MicroSegNet to capture global contextual dependencies and local information at various scales. We trained our model using micro-US images from 55 patients, followed by evaluation on 20 patients. Our MicroSegNet model achieved a Dice coefficient of 0.939 and a Hausdorff distance of 2.02 mm, outperforming several state-of-the-art segmentation methods, as well as three human annotators with different experience levels. Our code is publicly available at https://github.com/mirthAI/MicroSegNet and our dataset is publicly available at https://zenodo.org/records/10475293.

TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers.

Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into medical image segmentation. However, a comprehensive understanding of Transformers' self-attention in U-Net components is lacking. TransUNet, first introduced in 2021, is widely recognized as one of the first models to integrate Transformer into medical image analysis. In this study, we present the versatile framework of TransUNet that encapsulates Transformers' self-attention into two key modules: (1) a Transformer encoder tokenizing image patches from a convolution neural network (CNN) feature map, facilitating global context extraction, and (2) a Transformer decoder refining candidate regions through cross-attention between proposals and U-Net features. These modules can be flexibly inserted into the U-Net backbone, resulting in three configurations: Encoder-only, Decoder-only, and Encoder+Decoder. TransUNet provides a library encompassing both 2D and 3D implementations, enabling users to easily tailor the chosen architecture. Our findings highlight the encoder's efficacy in modeling interactions among multiple abdominal organs and the decoder's strength in handling small targets like tumors. It excels in diverse medical applications, such as multi-organ segmentation, pancreatic tumor segmentation, and hepatic vessel segmentation. Notably, our TransUNet achieves a significant average Dice improvement of 1.06% and 4.30% for multi-organ segmentation and pancreatic tumor segmentation, respectively, when compared to the highly competitive nn-UNet, and surpasses the top-1 solution in the BrasTS2021 challenge. 2D/3D Code and models are available at https://github.com/Beckschen/TransUNet and https://github.com/Beckschen/TransUNet-3D, respectively.

A generalist vision-language foundation model for diverse biomedical tasks.

Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outputs for diverse needs. However, existing biomedical generalist AI solutions are typically heavyweight and closed source to researchers, practitioners and patients. Here, we describe BiomedGPT, the first open-source and lightweight vision-language foundation model, designed as a generalist capable of performing various biomedical tasks. BiomedGPT achieved state-of-the-art results in 16 out of 25 experiments while maintaining a computing-friendly model scale. We also conducted human evaluations to assess the capabilities of BiomedGPT in radiology visual question answering, report generation and summarization. BiomedGPT exhibits robust prediction ability with a low error rate of 3.8% in question answering, satisfactory performance with an error rate of 8.3% in writing complex radiology reports, and competitive summarization ability with a nearly equivalent preference score to human experts. Our method demonstrates that effective training with diverse data can lead to more practical biomedical AI for improving diagnosis and workflow efficiency.

Vulnerability to HIV Infection Among International Immigrants in China: Cross-sectional Web-Based Survey.

BACKGROUND: The rising number of migrants worldwide, including in China given its recent rapid economic development, poses a challenge for the public health system to prevent infectious diseases, including sexually transmitted infections (STIs) caused by risky sexual behaviors. OBJECTIVE: The aim of this study was to explore the risky sexual behaviors of international immigrants living in China to provide evidence for establishment of a localized public health service system. METHODS: Risky sexual behaviors were divided into multiple sexual partners and unprotected sexual behaviors. Basic characteristics, sexual knowledge, and behaviors of international immigrants were summarized with descriptive statistics. Multivariate logistic regression analyses were used to identify factors associated with risky sexual behaviors, and the associations of demographic characteristics and risk behaviors with HIV testing and intention to test for HIV. RESULTS: In total, 1433 international immigrants were included in the study, 61.76% (n=885) of whom had never heard of STIs, and the mean HIV knowledge score was 5.42 (SD 2.138). Overall, 8.23% (118/1433) of the participants had been diagnosed with an STI. Among the 1433 international immigrants, 292 indicated that they never use a condom for homosexual sex, followed by sex with a stable partner (n=252), commercial sex (n=236), group sex (n=175), and casual sex (n=137). In addition, 119 of the international immigrants had more than three sex partners. Individuals aged 31-40 years were more likely to have multiple sexual partners (adjusted odds ratio [AOR] 2.364, 95% CI 1.149-4.862). Married participants were more likely to have unprotected sexual behaviors (AOR 3.096, 95% CI -1.705 to 5.620), whereas Asians were less likely to have multiple sexual partners (AOR 0.446, 95% CI 0.328-0.607) and unprotected sexual behaviors (AOR 0.328, 95% CI 0.219-0.492). Women were more likely to have taken an HIV test than men (AOR 1.413, 95% CI 1.085-1.841). Those who were married (AOR 0.577, 95% CI 0.372-0.894), with an annual disposable income >150,000 yuan (~US $22,000; AOR 0.661, 95% CI 0.439-0.995), considered it impossible to become infected with HIV (AOR 0.564, 95% CI 0.327-0.972), and of Asian ethnicity (AOR 0.330, 95% CI 0.261-0.417) were less likely to have an HIV test. People who had multiple sexual partners were more likely to have taken an HIV test (AOR 2.041, 95% CI 1.442-2.890) and had greater intention to test for HIV (AOR 1.651, 95% CI 1.208-2.258). CONCLUSIONS: International immigrants in China exhibit risky sexual behaviors, especially those aged over 30 years. In addition, the level of HIV-related knowledge is generally low. Therefore, health interventions such as targeted, tailored programming including education and testing are urgently needed to prevent new HIV infections and transmission among international immigrants and the local population.

Small-Object Sensitive Segmentation Using Across Feature Map Attention.

Semantic segmentation is an important step in understanding the scene for many practical applications such as autonomous driving. Although Deep Convolutional Neural Networks-based methods have significantly improved segmentation accuracy, small/thin objects remain challenging to segment due to convolutional and pooling operations that result in information loss, especially for small objects. This article presents a novel attention-based method called Across Feature Map Attention (AFMA) to address this challenge. It quantifies the inner-relationship between small and large objects belonging to the same category by utilizing the different feature levels of the original image. The AFMA could compensate for the loss of high-level feature information of small objects and improve the small/thin object segmentation. Our method can be used as an efficient plug-in for a wide range of existing architectures and produces much more interpretable feature representation than former studies. Extensive experiments on eight widely used segmentation methods and other existing small-object segmentation models on CamVid and Cityscapes demonstrate that our method substantially and consistently improves the segmentation of small/thin objects.

The Vulnerability of International Floating Populations to Sexually Transmitted Infections: A Qualitative Study.

With the rapid development of the global economy, along with globalisation, the health of international floating populations (especially their sexual health) has become a problem that cannot be ignored. This study explored the potential vulnerability of international floating populations to sexually transmitted infections (STIs) from the points of view of society, religion, culture, migration, community environment, and personal behaviours. In-depth exploratory interviews with 51 members of the international floating population living in China were conducted in June and July 2022. A qualitative thematic analysis methodology was used to analyse the content of these interviews. We found that a conservative culture orientated around religion leads to a lack of sex education, resulting in insufficient personal knowledge as well as a lack of the motivation and awareness required to encourage condom use during sexual contact. Additionally, both geographical isolation and reduced social supervision have expanded personal space, which has led to social isolation and marginalisation, in addition to challenges for coping with STI risk. These factors have increased the possibility of individuals engaging in risky behaviours.

Opioid-induced microbial dysbiosis disrupts irinotecan (CPT-11) metabolism and increases gastrointestinal toxicity in a murine model.

BACKGROUND AND PURPOSE: Opioids are commonly used for the management of cancer-associated pain and chemotherapy-induced diarrhoea. The chemotherapeutic irinotecan (CPT-11) causes severe gastrointestinal (GI) toxicity due to deconjugation of inactive metabolite SN-38 glucuronide (SN-38G) by bacterial ß-glucuronidases to the active 7-ethyl-10-hydroxycamptothecin (SN-38). Opioids are known to cause gut microbial dysbiosis, this study evaluated whether CPT-11 anti-tumour efficacy and GI toxicity are exacerbated by opioid co-administration. EXPERIMENTAL APPROACH: Eight-week-old C57BL/6 male mice were co-administration with CPT-11 ± opioid. 16S rRNA sequencing was used for gut microbiome analysis. LC-MS analyses of plasma and intestinal extracts were performed to investigate the pharmacokinetic profile of CPT-11. Histological analysis and quantitative real-time polymerase chain reaction were used to determine the severity of intestinal tissue damage. Human liver microsome In vitro assay was performed to confirm the effects of opioids on CPT-11 metabolism. KEY RESULTS: Gut microbiome analysis showed that morphine treatment induced enrichment of ß-glucuronidase-producing bacteria in the intestines of CPT-11-treated mice, resulting in SN-38 accumulation and exacerbation of GI toxicity in the small intestine. Oral administration of both antibiotics and glucuronidase inhibitor protected mice against GI toxicity induced with CPT-11 and morphine co-administration, implicating a microbiome-dependent mechanism. Additionally, morphine and loperamide decreased the plasma concentration of SN-38 and compromised CPT-11 anti-tumour efficacy, this seemed to be microbiome independent. CONCLUSION AND IMPLICATIONS: Gut microbiota play a significant role in opioid and chemotherapeutic agent drug-drug interactions. Inhibition of gut microbial glucuronidase may also prevent adverse GI effects of CPT-11 in patients on opioids.

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging.

The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervised pre-training paradigm that pre-trains models directly on decentralized target task datasets using masked image modeling, to facilitate more robust representation learning on heterogeneous data and effective knowledge transfer to downstream models. Extensive empirical results on simulated and real-world medical imaging non-IID federated datasets show that masked image modeling with Transformers significantly improves the robustness of models against various degrees of data heterogeneity. Notably, under severe data heterogeneity, our method, without relying on any additional pre-training data, achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training. In addition, we show that our federated self-supervised pre-training methods yield models that generalize better to out-of-distribution data and perform more effectively when fine-tuning with limited labeled data, compared to existing FL algorithms. The code is available at https://github.com/rui-yan/SSL-FL.

Patient-Specific Auto-segmentation on Daily kVCT Images for Adaptive Radiation Therapy.

PURPOSE: This study explored deep-learning-based patient-specific auto-segmentation using transfer learning on daily RefleXion kilovoltage computed tomography (kVCT) images to facilitate adaptive radiation therapy, based on data from the first group of patients treated with the innovative RefleXion system. METHODS AND MATERIALS: For head and neck (HaN) and pelvic cancers, a deep convolutional segmentation network was initially trained on a population data set that contained 67 and 56 patient cases, respectively. Then the pretrained population network was adapted to the specific RefleXion patient by fine-tuning the network weights with a transfer learning method. For each of the 6 collected RefleXion HaN cases and 4 pelvic cases, initial planning computed tomography (CT) scans and 5 to 26 sets of daily kVCT images were used for the patient-specific learning and evaluation separately. The performance of the patient-specific network was compared with the population network and the clinical rigid registration method and evaluated by the Dice similarity coefficient (DSC) with manual contours being the reference. The corresponding dosimetric effects resulting from different auto-segmentation and registration methods were also investigated. RESULTS: The proposed patient-specific network achieved mean DSC results of 0.88 for 3 HaN organs at risk (OARs) of interest and 0.90 for 8 pelvic target and OARs, outperforming the population network (0.70 and 0.63) and the registration method (0.72 and 0.72). The DSC of the patient-specific network gradually increased with the increment of longitudinal training cases and approached saturation with more than 6 training cases. Compared with using the registration contour, the target and OAR mean doses and dose-volume histograms obtained using the patient-specific auto-segmentation were closer to the results using the manual contour. CONCLUSIONS: Auto-segmentation of RefleXion kVCT images based on the patient-specific transfer learning could achieve higher accuracy, outperforming a common population network and clinical registration-based method. This approach shows promise in improving dose evaluation accuracy in RefleXion adaptive radiation therapy.

Organ-specific fuel rewiring in acute and chronic hypoxia redistributes glucose and fatty acid metabolism.

Oxygen deprivation can be detrimental. However, chronic hypoxia is also associated with decreased incidence of metabolic syndrome and cardiovascular disease in high-altitude populations. Previously, hypoxic fuel rewiring has primarily been studied in immortalized cells. Here, we describe how systemic hypoxia rewires fuel metabolism to optimize whole-body adaptation. Acclimatization to hypoxia coincided with dramatically lower blood glucose and adiposity. Using in vivo fuel uptake and flux measurements, we found that organs partitioned fuels differently during hypoxia adaption. Acutely, most organs increased glucose uptake and suppressed aerobic glucose oxidation, consistent with previous in vitro investigations. In contrast, brown adipose tissue and skeletal muscle became "glucose savers," suppressing glucose uptake by 3-5-fold. Interestingly, chronic hypoxia produced distinct patterns: the heart relied increasingly on glucose oxidation, and unexpectedly, the brain, kidney, and liver increased fatty acid uptake and oxidation. Hypoxia-induced metabolic plasticity carries therapeutic implications for chronic metabolic diseases and acute hypoxic injuries.

HIV Detection and Delayed Diagnosis: A Time Series Analysis in China.

BACKGROUND: Insufficient HIV detection and late presentation to antiretroviral therapy (ART) pose significant public health challenges. In China, universal access to HIV testing is available now. Under this background, we aim to analyze the trends of HIV detection and the prevalence of delayed HIV diagnosis (DHD) in order to provide evidence for HIV prevention and treatment in China. METHODS: Data of HIV tests in Hangzhou city between 2007 and 2018 were collected from the Chinese National HIV/AIDS Comprehensive Response Information Management System (CRIMS). Descriptive statistics were used to describe the characteristics of HIV testing and detection and the prevalence of DHD among newly diagnosed HIV cases. Non-parametric tests were employed to examine the prevalence of DHD among newly diagnosed HIV cases. Moreover, logistic regression models were employed to explore the influencing factors of DHD. RESULTS: Testing rates doubled from 14.1% in 2007-2010 to 28.2% in 2016-2018. The total positive rate of HIV tests was 5.3 per 10,000. Preoperative testing was the predominant pathway for HIV tests, accounting for 41.9%, followed by testing for health screening, maternal examination and other patients, accounting for 18.4%, 13.2% and 11.8%, respectively. Meanwhile, the predominant pathway for HIV case detection was also preoperative testing, accounting for 29.1%, followed by testing for other patients, testing at STD clinics and VCT, with the proportions of 18.8%, 15.8% and 13.6%, respectively. MSM (men who have sex with men) contact was the main transmission route, accounting for 55.3%, followed by heterosexual contact, accounting for 41.6%. Overall, DHD occurred in 29.0% of the newly diagnosed cases, and this rate had not improved over the years. A higher prevalence of DHD was found in those diagnosed through a pre-test for receiving blood/products [OR (95%CI): 5.42(2.95-9.97)], detection of other patients [OR (95%CI): 2.08(1.64-2.63)], preoperative testing [OR (95%CI): 1.83(1.44-2.32)] and spouse or sexual partner testing in positive person [OR (95%CI): 1.93(1.34-2.78)] compared with those diagnosed at a VCT clinic. Heterosexuals [OR (95%CI): 1.20(1.06-1.36)] had a higher risk of DHD than MSM. Diagnosis at a CDC [OR (95%CI): 0.68(0.55-0.83)] and community health centers [OR (95%CI): 0.54(0.39-0.75)] had a lower risk of DHD than in hospitals. Older age, males, being single/divorced or widowed and floating population were also associated with DHD. CONCLUSIONS: In China, DHD had not improved in the last 10 years, although HIV testing had been expanded. Therefore, it is important for continued efforts to promote early diagnosis of HIV to prevent transmission, morbidity and early mortality in HIV infection.