A new method for the rapid identification of external water types in rainwater pipeline networks using UV-Vis absorption spectroscopy.

Ultraviolet-visible (UV-Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV-Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks.

High-Throughput RPLC-MS/MS Quantification of Short- and Medium-Chain Fatty Acids.

Short- and medium-chain fatty acids (SMCFA) are monocarboxylic acids with a carbon chain length of 1-12 carbon atoms. They are mainly produced in humans by the gut microbiota, play crucial metabolic roles, are vital for intestinal health, and have multifaceted impact on immune and neurological functions. Accurate detection and quantification of SMCFA in different human biofluids is achieved using 3-nitro phenylhydrazine (3-NPH) derivatization of the free fatty acids followed by reverse phase liquid chromatography (RPLC) separation and detection by tandem mass spectrometry (MS/MS). Here, we describe the simultaneous measurement of 14 SMCFA and lactate in detail. All 3-NPH-SMCFA-hydrazones are separated in less than 5 min with an 8-min total run time (injection-to-injection). Linear dynamic range over 0.1-500 µM is achieved for most SCFAs, while it is 0.05-100 µM for MCFAs. Validation of the procedure depicts good linearity (R2 > 0.98) and repeatability (CV ≤ 20%). The lower limit of detection (LLOD) is 10-30 nM. The lower limit of quantification (LLOQ) is 50-100 nM for most analytes, while it is 0.5 µM for acetate. In conclusion, the method offers several benefits compared to alternative methods regarding throughput, selectivity, sensitivity, and robustness.

Decoding the genetic landscape of autism: A comprehensive review.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by heterogeneous symptoms and genetic underpinnings. Recent advancements in genetic and epigenetic research have provided insights into the intricate mechanisms contributing to ASD, influencing both diagnosis and therapeutic strategies. AIM: To explore the genetic architecture of ASD, elucidate mechanistic insights into genetic mutations, and examine gene-environment interactions. METHODS: A comprehensive systematic review was conducted, integrating findings from studies on genetic variations, epigenetic mechanisms (such as DNA methylation and histone modifications), and emerging technologies [including Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 and single-cell RNA sequencing]. Relevant articles were identified through systematic searches of databases such as PubMed and Google Scholar. RESULTS: Genetic studies have identified numerous risk genes and mutations associated with ASD, yet many cases remain unexplained by known factors, suggesting undiscovered genetic components. Mechanistic insights into how these genetic mutations impact neural development and brain connectivity are still evolving. Epigenetic modifications, particularly DNA methylation and non-coding RNAs, also play significant roles in ASD pathogenesis. Emerging technologies like CRISPR-Cas9 and advanced bioinformatics are advancing our understanding by enabling precise genetic editing and analysis of complex genomic data. CONCLUSION: Continued research into the genetic and epigenetic underpinnings of ASD is crucial for developing personalized and effective treatments. Collaborative efforts integrating multidisciplinary expertise and international collaborations are essential to address the complexity of ASD and translate genetic discoveries into clinical practice. Addressing unresolved questions and ethical considerations surrounding genetic research will pave the way for improved diagnostic tools and targeted therapies, ultimately enhancing outcomes for individuals affected by ASD.

Suppression of certain intestinal microbiota metabolites may lead to gestational diabetes in mice fed a high-fat diet.

Background: We aim to establish a gestational diabetes mellitus (GDM) mouse model with mice fed with a high-fat diet (HFD) in comparison with pregnant mice with normal blood glucose levels to investigate the role of intestinal microbiota in the development of HFD-induced GDM. Methods: We divided healthy 6-week-old female C57BL mice into an HFD-induced GDM group and a normal diet group. Their bacterial flora and metabolites in intestinal fecal exosomes were co-analyzed using 16 s multi-region sequencing and compared. Findings: Alpha (α) diversity was lower within the model group compared to the control group. Beta (ß) diversity was significantly different between the two groups. The relative abundances of Lactobacillus, Actinomyces, Rothia, and Bacteroidetes were significantly different between the two groups. Fermentation and nitrate consumption were significantly higher in the GDM group. Multiple bacteria were associated with glycerophosphocholine, S-methyl-5'-thioadenosine, quinolinate, galactinol, deoxyadenosine, DL-arginine, and 2-oxoadenic acid. Interpretation: Imbalances in the production of Lactobacillus, Bacteroidetes, Actinomyces, and Rothia and their related metabolites may lead to metabolic disturbances in GDM. These indicators may be used to assess changes affecting the intestinal microbiota during pregnancy and thus help modulate diet and alter blood glucose.

Combined proteomics and metabolomics analysis reveal the effect of a training course on the immune function of Chinese elite short-track speed skaters.

INTRODUCTION: The aim of this study was to combine proteomics and metabolomics to evaluate the immune system of short-track speed skaters (STSS) before and after a training course. Our research focused on changes in urinary proteins and metabolites that have the potential to serve as indicators for training load. METHODS: Urine samples were collected from 21 elite STSS (13 male and 8 female) of the China National Team before and immediately after one training course. First-beat sports sensor was used to monitor the training load. Proteomic detection was performed using a Thermo UltiMate 3000 ultra high performence chromatography nano liquid chromatograph and an Orbitrap Exploris 480 mass spectrometer. MSstats (R package) was used for the statistical evaluation of significant differences in proteins from the samples. Two filtration criteria (fold change [FC] > 2 and p < 0.05) were used to identify the differential expressed proteins. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis for differential proteins was performed to identify the pathways involved. Nontargeted metabolomic detection was performed using ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS_) with an ACQUITY 2D UPLC plus Q Exactive (QE) hybrid Quadrupole-Orbitrap mass spectrometer. Differential metabolites were identified using non-parametric statistical methods (Wilcox's rank test). Two filtration criteria (FC > 1.2 and p < 0.05) were used to identify differential metabolites. Combined analysis of proteomic and metabolomics were performed on the "Wu Kong" platform. Correlation analysis was performed using Spearman's rank correlation coefficient. RESULTS: (1) The most upregulated proteins were immune-related proteins, including complement proteins (C9, C4-B, and C9) and immunoglobulins (IgA, IgM, and IgG). The most downregulated proteins were osteopontin (OPN) and CD44 in urine. The correlation analysis showed that the content of OPN and CD44 (the receptor for OPN) in urine were significantly negatively correlated with the upregulated immune-related proteins. The content of OPN and CD44 is sex-dependent and negatively correlated with the training load. (2) The most upregulated metabolites included lactate, cortisol, inosine, glutamine, argininosuccinate (the precursor for arginine synthesis), 3-methyl-2-oxobutyrate (the catabolite of valine), 3-methyl-2-oxovalerate (the catabolite of isoleucine), and 4-methyl-2-oxopentanoate (the catabolite of leucine), which is sex-dependent and negatively correlated with OPN and CD44. (3) The joint analysis revealed five main related pathways, including the immune and innate immune systems. The enriched immune-related proteins included complements, immunoglobulins, and protein catabolism-related proteins. The enriched immune-related metabolites included cAMP, N-acetylgalactosamine, and glutamate. (4) There is a significant negative correlation between the content of OPN and CD44 in urine and the training load. CONCLUSION: One training course can lead to the activation of the immune system and a sex-dependent decrease in the content of OPN and CD44. Training load has a significant and negative correlation with the content of OPN and CD44, suggesting that OPN and CD44 could be potential indicators for training load.

Microbes and mood: innovative biomarker approaches in depression.

Although the field of psychiatry has made gains in biomarker discovery, our ability to change long-term outcomes remains inadequate. Matching individuals to the best treatment for them is a persistent clinical challenge. Moreover, the development of novel treatments has been hampered in part due to a limited understanding of the biological mechanisms underlying individual differences that contribute to clinical heterogeneity. The gut microbiome has become an area of intensive research in conditions ranging from metabolic disorders to cancer. Innovation in these spaces has led to translational breakthroughs, offering novel microbiome-informed approaches that may improve patient outcomes. In this review we examine how translational microbiome research is poised to advance biomarker discovery in mental health, with a focus on depression.

The minimal clinically important difference of two multifaceted fatigue evaluation questionnaires in chronic stroke.

PURPOSE: This research sought to ascertain the Minimal Clinically Important Difference (MCID) and Robust Clinically Important Difference (RCID) of the Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF) and Chalder Fatigue Questionnaire-11 (CFQ-11) as two important concepts for the clinical interpretation of the results in chronic post-stroke population. METHODS: A total of 128 subjects with chronic post-stroke completed the MFSI-SF and CFQ-11 before and after six weeks of intervention. The MCIDs were derived using both anchor- and distribution-based methods; however, only anchor-based methods were used to estimate RCIDs. RESULTS: Anchor-based MCIDs for MFSI-SF and CFQ-11 were in the range of -5 to -6.28 and -2 to -4.56, respectively. Distribution-based MCIDs in MFSI-SF and CFQ-11 were calculated in the range of -4.17 to -24.05 and -1.72 to -7.68, respectively. RCID ranges of -10 to -15 were obtained for the MFSI-SF and -6 to -7.33 for the CFQ-11. CONCLUSION: These findings may have implications for clinical experts in the clinical interpretation of fatigue changes observed in MFSI-SF and CFQ-11 in individuals with chronic stroke.

Impact of a short-term pharmacy study abroad Program: student outcomes and program evaluation.

OBJECTIVE: This study examined the impact of a short-term study abroad program, focusing on program evaluation, attendee satisfaction, and acquired knowledge and skills. A questionnaire survey was conducted covering various aspects including demographics, program evaluation, and feedback. RESULTS: Results indicated higher female participation due to gender imbalances in pharmacy students in Egypt, with senior students recognizing the value of international experience. Attendee satisfaction was high, with positive feedback on accommodation, tours, and workshop materials. Field visits and workshops provided valuable experiential learning, with attendees suggesting extending the program's duration. The program equipped attendees with knowledge and skills relevant to pharmaceutical products and services, leading to improved competences and perceptions. The study concludes that such study abroad experiences profoundly impact personal growth and recommends integrating them into educational curricula for valuable experiences.

Squamous cell carcinoma of the cervix associated with choriocarcinomatous differentiation: a case report and review of the literature.

The morphology of the choriocarcinomatous variant of cervical squamous cell carcinoma (SCC) suggests an undifferentiated aggressive biological behaviour and a poor outcome, for which standard treatment has not been established. In addition, cases are rarely reported, with only five cases of patients with cervical carcinoma with choriocarcinoma reported previously in the literature. This current case report describes in detail a patient who was diagnosed with cervical SCC mixed with choriocarcinomatous differentiation. The case report includes details of the diagnosis, pathology, short tandem repeat genotyping, treatment and follow-up of this patient. As there is no standard treatment for this variant, the patient underwent surgery followed by radiotherapy. Unfortunately, 4 months after therapy discontinuation, radiological evaluation and laboratory tests documented a recurrence of the disease and the patient died. This report also systematically reviews the literature on cervical cancer associated with choriocarcinomatous differentiation and the five previous cases. It provides the most up-to-date summary of this disease, including its clinical manifestations, histopathology, diagnosis, treatment and prognosis.

Machine learning-based modeling of surface water temperature dynamics in arctic lakes.

Lake surface-water temperature (LSWT) regulates physical and biochemical processes in lakes. Therefore, understanding the LSWT dynamics is important, especially in Arctic zone since the region is experiencing a warming rate that is greater than the Earth's average. However, regular measurements of LSWT in the remote Arctic lakes always face difficulties or cannot be done by satellites accurately due to the cloud cover and their limited spatiotemporal resolution. Here, we used a historically rich data (1960-2023) to develop four machine learning-based algorithms for the daily LSWT modeling in Lake Inari, situated in Arctic zone, using the air-temperature data. Our results showed that both air-temperature (0.030 °C/yr) and LSWT (0.023m °C/yr) were warming with a rate faster than those in the globe. The long-short-term memory model, with the coefficients of determination varied from 0.96 to 0.98, outperformed other algorithms in modeling of the daily LSWT dynamics in Lake Inari, followed by both support vector regression and neural network tools, and random forest model. As the air-temperature data are widely accessible through synoptic stations and remote sensing techniques, our suggested models can be simply adopted for other Arctic lakes, where the local water-temperature data are often lacking or contain large windows of missing data due to harsh atmospheric conditions and equipment failure.