Optimization of Multiplex-PCR Technique To Determine Azf Deletions in infertility Male Patients

Tung Nguyen Thanh,1 Sang Trieu Tien,2 Phong Nguyen Van,2 Son Dang Thai,3 Thuc Luong Cong,4 Tuan Dinh Le,5 Son Tien Nguyen,5 Tuan Tran Van,1 Hoang Huy Duong,6 Tien Minh Bui,7 Kien Trung Nguyen7 1Military Institute of Clinical Embryology and Histology, Vietnam Military Medical University, Hanoi, 100000, Vietnam; 2Department of Biology and Medical Genetics, Vietnam Military Medical University, Hanoi, 100000, Vietnam; 3Institute of Biological and Food Technology, Hanoi Open University, Hanoi, 100000, Vietnam; 4Cardiovascular Center, Military Hospital 103, Vietnam Military Medical University, Hanoi, 100000, Vietnam; 5Department of Rheumatology and Endocrinology, Military Hospital 103, Vietnam Military Medical University, Hanoi, 100000, Vietnam; 6Department of Neurology, Thai Binh University of Medicine and Pharmacy, Thai Binh, 410000, Vietnam; 7Department of Obstetrics and Gynecology, Thai Binh University of Medicine and Pharmacy, Thai Binh, 410000, VietnamCorrespondence: Sang Trieu Tien, Department of Biology and Medical Genetics, Vietnam Military Medical University, Hanoi, 100000, Vietnam, Email [email protected]: To optimize the multiplex polymerase chain reaction (M-PCR) technique to diagnose microdeletions of azoospermia factors (AZF) on the Y chromosome and initially apply the technique to diagnose male patients with sperm density less than 5× 106 million sperm/mL was assigned to do a test to check for AZF microdeletions on the Y chromosome.Methods: Based on the positive control samples which belong to male subjects who have had 2 healthy children without any assisted reproductive technologies, the M-PCR method was developed to detect simultaneously and accurately AZF microdeletions on 32 male patients with sperm densities below 5× 106 million sperm/mL of semen at the Department of Biology and Medical Genetics – Vietnam Military Medical University.Results: Successful optimization of the M-PCR technique including 7 reactions arranged according to each AZFabc region using 24 STS/gene on the Y chromosome. Initial application to diagnose AZF deletion on 32 azoospermic and oligospermic men reveals that AZFa deletion accounts for 6.25% (2/32); deletion of all 3 regions AZFa,b,c with 18.75% (6/32 cases); The combined deletion rate of AZFb,c is highest, accounting for 56.24% (18/32 patients).Conclusion: Successfully optimized the M-PCR technique in identifying AZF microdeletions using 24 sequence tagged sites (STS)/gene for azoospermic and oligozoospermic men. The M-PCR technique has great potential in the application of AZF deletion diagnosis.Keywords: male infertility, azoospermia factors, AZF, multiplex polymerase chain reaction, M-PCR, sequence tagged sites, ST

Water level prediction using deep learning models: A case study of the Kien Giang River, Quang Binh Province

Abstract Time‐series water level prediction during natural disasters, for example, typhoons and storms, is crucial for both flood control and prevention. Utilizing data‐driven models that harness deep learning (DL) techniques has emerged as an attractive and effective approach to water level prediction. This paper proposed an innovative data‐driven methodology using DL network architectures of Gated Recurrent Unit (GRU), Long Short‐Term Memory (LSTM), and Bidirectional Long‐Short Term Memory (Bi‐LSTM) to predict the water level at the Le Thuy station in the Kien Giang River. These models were implemented and validated based on hourly rainfall and water level observations at meteo‐hydrological stations. Three combinations of input variables with different time leads and time lags were established to evaluate the forecast capability of three proposed models by using five metrics, that is, R2, MAE, RMSE, Max Error Value, and Max Error Time. The results revealed that the LSTM model outperformed the Bi‐LSTM and GRU models, when water level and rainfall observations for one‐time lag at three stations were used to predict the water level at the Le Thuy station with 1‐h time lead, with the five metrics registering at 0.999; 3.6 cm; 2.6 cm; 12.9 cm; and −1 h, respectively

Long-term outcome in survivors of neonatal tetanus following specialist intensive care in Vietnam

Abstract Background Neonatal tetanus continues to occur in many resource-limited settings but there are few data regarding long-term neurological outcome from the disease, especially in settings with critical care facilities. Methods We assessed long-term outcome following neonatal tetanus in infants treated in a pediatric intensive care unit in southern Vietnam. Neurological and neurodevelopmental testing was performed in 17 survivors of neonatal tetanus and 18 control children from the same communities using tools previously validated in Vietnamese children. Results The median age of children assessed was 36 months. Eight neonatal tetanus survivors and 9 community control cases aged < 42 months were tested using the Bayley III Scales of Infant and Toddler Development (Bayley III-VN) and 8 neonatal tetanus survivors and 9 community controls aged ≥42 months were tested using the Movement Assessment Battery for Children. No significant reductions in growth indices or neurodevelopmental scores were shown in survivors of neonatal tetanus compared to controls although there was a trend towards lower scores in neonatal tetanus survivors. Neurological examination was normal in all children except for two neonatal tetanus survivors with perceptive deafness and one child with mild gross motor abnormality. Neonatal tetanus survivors who had expienced severe disease (Ablett grade ≥ 3) had lower total Bayley III-VN scores than those with mild disease (15 (IQR 14–18) vs 24 (IQR 19–27), p = 0.05) with a significantly lower cognitive domain score (3 (IQR 2–6) severe disease vs 7 (IQR 7–8) mild disease, p = 0.02). Conclusions Neonatal tetanus is associated with long-term sequelae in those with severe disease. In view of these findings, prevention of neonatal tetanus should remain a priority

Metformin as adjunctive therapy for dengue in overweight and obese patients: a protocol for an open-label clinical trial (MeDO)

Background: Dengue is a disease of major global importance. While most symptomatic infections are mild, a small proportion of patients progress to severe disease with risk of hypovolaemic shock, organ dysfunction and death. In the absence of effective antiviral or disease modifying drugs, clinical management is solely reliant on supportive measures. Obesity is a growing problem among young people in Vietnam and is increasingly recognised as an important risk factor for severe dengue, likely due to alterations in host immune and inflammatory pathways. Metformin, a widely used anti-hyperglycaemic agent with excellent safety profile, has demonstrated potential as a dengue therapeutic in vitro and in a retrospective observational study of adult dengue patients with type 2 diabetes. This study aims to assess the safety and tolerability of metformin treatment in overweight and obese dengue patients, and investigate its effects on several clinical, immunological and virological markers of disease severity. Methods: This open label trial of 120 obese/overweight dengue patients will be performed in two phases, with a metformin dose escalation if no safety concerns arise in phase one. The primary endpoint is identification of clinical and laboratory adverse events. Sixty overweight and obese dengue patients aged 10-30 years will be enrolled at the Hospital for Tropical Diseases in Ho Chi Minh City, Vietnam. Participants will complete a 5-day course of metformin therapy and be compared to a non-treated group of 60 age-matched overweight and obese dengue patients. Discussion: Previously observed antiviral and immunomodulatory effects of metformin make it a promising dengue therapeutic candidate in appropriately selected patients. This study will assess the safety and tolerability of adjunctive metformin in the management of overweight and obese young dengue patients, as well as its effects on markers of viral replication, endothelial dysfunction and host immune responses. Trial registration: ClinicalTrials.gov: NCT04377451 (May 6th 2020)

Learning meaningful latent space representations for patient risk stratification: model development and validation for dengue and other acute febrile illness

Background: Increased data availability has prompted the creation of clinical decision support systems. These systems utilise clinical information to enhance health care provision, both to predict the likelihood of specific clinical outcomes or evaluate the risk of further complications. However, their adoption remains low due to concerns regarding the quality of recommendations, and a lack of clarity on how results are best obtained and presented. Methods: We used autoencoders capable of reducing the dimensionality of complex datasets in order to produce a 2D representation denoted as latent space to support understanding of complex clinical data. In this output, meaningful representations of individual patient profiles are spatially mapped in an unsupervised manner according to their input clinical parameters. This technique was then applied to a large real-world clinical dataset of over 12,000 patients with an illness compatible with dengue infection in Ho Chi Minh City, Vietnam between 1999 and 2021. Dengue is a systemic viral disease which exerts significant health and economic burden worldwide, and up to 5% of hospitalised patients develop life-threatening complications. Results: The latent space produced by the selected autoencoder aligns with established clinical characteristics exhibited by patients with dengue infection, as well as features of disease progression. Similar clinical phenotypes are represented close to each other in the latent space and clustered according to outcomes broadly described by the World Health Organisation dengue guidelines. Balancing distance metrics and density metrics produced results covering most of the latent space, and improved visualisation whilst preserving utility, with similar patients grouped closer together. In this case, this balance is achieved by using the sigmoid activation function and one hidden layer with three neurons, in addition to the latent dimension layer, which produces the output (Pearson, 0.840; Spearman, 0.830; Procrustes, 0.301; GMM 0.321). Conclusion: This study demonstrates that when adequately configured, autoencoders can produce two-dimensional representations of a complex dataset that conserve the distance relationship between points. The output visualisation groups patients with clinically relevant features closely together and inherently supports user interpretability. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management