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Palm oil mill effluent (POME), a byproduct of palm oil processing, has substantial resource recovery potential. Its rich biodegradable content supports methane (CH4) production via anaerobic digestion, enabling renewable energy generation. Additionally, the significant water content of POME can be reclaimed for use in boiler feed, irrigation, and drinking water. However, selecting appropriate technologies to recover valuable resources from POME is challenging, particularly for the purification and upgrading of biogas. Membrane technologies offer an effective approach for transforming POME treatment from an energy-intensive process into a resource recovery system, supporting the decarbonization of palm oil production and advancing global sustainability objectives. This technique is cost-effective and ecofriendly for biogas purification and water reclamation. For biogas purification and upgrading, membrane systems offer the lowest capital and operational costs at 5.654 USD/m3, compared to other technologies, such as 6.249 USD/m3 for water scrubbers and 6.999 USD/m3 for chemical absorbers. This review primarily explores the potential of membranes for gas purification from POME and examines their integration with other processes to develop advanced systems, such as ultrasonicated membrane anaerobic systems and membrane anaerobic systems, to enhance biogas production. In addition, water reclamation from POME is discussed, with ultrafiltration membranes emerging as the most promising candidates. Proton exchange membranes, such as Nafion, are used extensively in microbial fuel cells to improve electricity generation, and this is also summarized. Finally, challenges and future perspectives are highlighted, emphasizing the broader potential of membrane technology in POME wastewater resource recovery.

Wastewater sludge is used as an alternative fuel due to its high organic content and calorific value. However, influent characteristics and operational practices of wastewater treatment plants (WWTPs) can increase the sulfur content of sludge, devaluing it as a fuel. Thus, we investigated the biochemical mechanisms that elevate the sulfur content of sludge in a full-scale industrial WWTP receiving wastewater of the textile dyeing industry and a domestic WWTP by monitoring the sulfate, sulfur, and iron contents and the biochemical transformation of sulfate to sulfur in the wastewater and sludge treatment streams. A batch sulfate reduction rate test and microbial 16S rRNA and dsrB gene sequencing analyses were applied to assess the potential and activity of sulfate-reducing bacteria and their effect on sulfur deposition. This study indicated that the primary clarifier and anaerobic digester prominently reduced sulfate concentration through biochemical sulfate reduction and iron–sulfur complexation under anaerobic conditions, from 1247 mg/L in the influent to 6.2~59.8 mg/L in the industrial WWTP and from 46.7 mg/L to 0~0.8 mg/L in the domestic WWTPs. The anaerobic sludge, adapted in the high sulfate concentration of the industrial WWTP, exhibited a two times higher specific sulfate reduction rate (0.13 mg SO42−/gVSS/h) and sulfur content (3.14% DS) than the domestic WWTP sludge. Gene sequencing analysis of the population structure of common microbes and sulfate-reducing bacteria indicated the diversity of microorganisms involved in biochemical sulfate reduction in the sulfur cycle, supporting the data revealed by chemical analysis and batch tests.

Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format along with the labels of both the training set and the test set.Measurement(s)diseases and abnormal findings from chest X-ray scansTechnology Type(s)AI is used to detect diseases and abnormal findingsSample Characteristic - LocationVietnam

Background Late detection of hepatocellular carcinoma (HCC) results in an overall 5-year survival rate of less than 16%. Liquid biopsy (LB) assays based on detecting circulating tumor DNA (ctDNA) might provide an opportunity to detect HCC early noninvasively. Increasing evidence indicates that ctDNA detection using mutation-based assays is significantly challenged by the abundance of white blood cell-derived mutations, non-tumor tissue-derived somatic mutations in plasma, and the mutational tumor heterogeneity. Methods Here, we employed concurrent analysis of cancer-related mutations, and their fragment length profiles to differentiate mutations from different sources. To distinguish persons with HCC (PwHCC) from healthy participants, we built a classification model using three fragmentomic features of ctDNA through deep sequencing of thirteen genes associated with HCC. Results Our model achieved an area under the curve (AUC) of 0.88, a sensitivity of 89%, and a specificity of 82% in the discovery cohort consisting of 55 PwHCC and 55 healthy participants. In an independent validation cohort of 54 PwHCC and 53 healthy participants, the established model achieved comparable classification performance with an AUC of 0.86 and yielded a sensitivity and specificity of 81%. Conclusions Our study provides a rationale for subsequent clinical evaluation of our assay performance in a large-scale prospective study.

In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating T cell receptor β chain (TCRβ) profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRβ sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes (TILs) from 28 CRC patients. The data unveiled both intra-tumor and inter-patient heterogeneity in the TCRβ repertoires of CRC patients, likely resulting from the stochastic utilization of V and J segments in response to neoantigens. Our novel combined model integrates pHLA binding information with pHLA-TCR binding to prioritize neoantigens, resulting in heightened specificity and sensitivity compared to models using individual features alone. The efficacy of our proposed model was corroborated through ELISpot assays on long peptides, performed on four CRC patients. These assays demonstrated that neoantigen candidates prioritized by our combined model outperformed predictions made by the established tool NetMHCpan. This comprehensive assessment underscores the significance of integrating pHLA binding with pHLA-TCR binding analysis for more effective immunotherapeutic strategies.

Background In our previous study, we demonstrated that headaches are highly prevalent among medical students in Vietnam. In the present study, we provide estimates of the associated symptom burden and impaired participation, utilizing these estimates to assess headache-related healthcare needs within this population. Methods The study followed the standardized methodology established by the Global Campaign against Headache. Participants included medical students who were randomly selected from two medical universities in Vietnam. Data collection utilized the HARDSHIP questionnaire, which included diagnostic questions based on ICHD-3 criteria, measures of symptom burden, quality of life (QoL) assessments using the WHOQoL-8, evaluations of impaired participation through the HALT index, and questions about headache yesterday (HY). The definition of health care “need” was based on the likelihood of benefit from intervention, including all participants with probable medication-overuse headache (pMOH), other headaches occurring on ≥ 15 days/month (H15+), migraine on ≥ 3 days/month, or migraine or tension-type headache (TTH) meeting at least one of two criteria related to symptom burden and impaired participation. Results A total of 1,362 participants (57.3% female) were included, of whom 1,125 students (61.3% female) were diagnosed with a headache disorder, and 165 students (69.1% female) reported experiencing a HY. The mean frequency of any headache was 3.6 days per month, with an average duration of 5.3 h, and 58% of participants reported an intensity of moderate/severe. For all headache, the mean pTIS was 2.8%. The mean number of lost days over a period of 3 months was 4.3 for work/school tasks, 3.8 for household chore, and 1.7 for social or leisure activities. Among those reporting a HY, 35.8% were able to complete less than half of their expected activities, while 9.7% could complete none. QoL of students with any headache was significantly lower than that of students without headache. A mong students with headache, 43.8% fulfilled atleast one of our needs assessment criteria. Conclusions This first study on headache burden in Vietnam reveals substantial symptom burden alongside a correspondingly high level of impaired participation among medical students.

In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating T cell receptor β chain (TCRβ) profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRβ sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes (TILs) from 28 CRC patients. The data unveiled both intra-tumor and inter-patient heterogeneity in the TCRβ repertoires of CRC patients, likely resulting from the stochastic utilization of V and J segments in response to neoantigens. Our novel combined model integrates pHLA binding information with pHLA-TCR binding to prioritize neoantigens, resulting in heightened specificity and sensitivity compared to models using individual features alone. The efficacy of our proposed model was corroborated through ELISpot assays on long peptides, performed on four CRC patients. These assays demonstrated that neoantigen candidates prioritized by our combined model outperformed predictions made by the established tool NetMHCpan. This comprehensive assessment underscores the significance of integrating pHLA binding with pHLA-TCR binding analysis for more effective immunotherapeutic strategies.

PurposeGonadotropin-resistant ovary syndrome (GROS) is a rare endocrine disorder that causes hypergonadotropic hypogonadism, amenorrhea, and infertility. This study reports live birth in two women with GROS who underwent fertility treatment with in vitro maturation (IVM).MethodsBoth patients had primary infertility, amenorrhea (primary and secondary), typical secondary sexual characters, elevated gonadotropin levels, normal ovarian reserve, normal chromosomal characteristics, and previous nonresponsiveness gonadotropin stimulations. One patient had polymorphism of the follicle-stimulating hormone receptor, which is a predictor of poor ovarian response. Given unresponsiveness to exogenous gonadotropin stimulations, IVM with human chorionic gonadotropin priming (hCG-IVM) was performed in both patients. All transferrable embryos were vitrified.ResultsBoth patients achieved pregnancy after their first frozen embryos transfer, and each delivered a healthy baby boy.ConclusionsThese results suggest that IVM should be a first-line therapeutic option for patients with GROS.

Immunization is the most cost-effective health strategy, contributing significantly to public health interventions for all ages, particularly for children. However, caregivers' satisfaction with immunization systems affects their decisions on immunization for their children. This study evaluated the levels of clients' satisfaction toward child immunization and to identify its associated factors. A cross-sectional study was conducted at 40 commune health centers (CHCs) in 24 districts in Ho Chi Minh City, Vietnam among 1200 caregivers of children aged under 5 years. Clients who took their children to CHCs for immunization were recruited based on convenience sampling technique and were asked to complete a self-report questionnaire. Satisfaction was measured using the Satisfaction with Immunization Service Questionnaire (SWISQ). Ordinal logistic regression models were fitted to identify factors associated with satisfaction levels. The majority of participants were female (85.5%) with a mean age of 33.3 (standard deviation = 9.0). Approximately 60% of participants reported a moderate (40.2%) or high (17.1%) level of satisfaction. Participants with older children and those who waited for a longer duration had a lower satisfaction level. In contrast, high satisfaction level was found to be positive associated with being reminded by healthcare workers and the condition of follow-up areas, vaccine storage and the immunization process met participant's need. The level of clients' satisfaction toward child immunization at grassroot healthcare centers in Ho Chi Minh City is relatively low, with 40.2% having moderate satisfaction and 17.1% having high satisfaction. Strategies to improve vaccination programs at CHCs are needed, focusing on clients' experiences at CHCs during vaccination sessions. Further studies are also needed to have an in-depth understanding of more factors affecting satisfaction in this population.

In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating TCRbeta profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRbeta sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes from 27 CRC patients. The data unveiled both intra-tumor and inter-patient heterogeneity in the TCRbeta repertoires of CRC patients, likely resulting from the stochastic utilization of V and J segments in response to neoantigens. Our novel combined model integrates pHLA binding information with pHLA-TCR binding to prioritize neoantigens, resulting in heightened specificity and sensitivity compared to models using individual features alone. The efficacy of our proposed model was corroborated through ELISpot assays on long peptides, performed on four CRC patients. These assays demonstrated that neoantigen candidates prioritized by our combined model outperformed predictions made by the established tool NetMHCpan. This comprehensive assessment underscores the significance of integrating pHLA binding with pHLA-TCR binding analysis for more effective immunotherapeutic strategies.Competing Interest StatementThis research was funded by a NexCalibur Therapeutic grant (NC01). The authors including LST and MDP hold the equity in NexCalibur Therapeutic.