Principal Component Analysis Research Articles

Impact of water physicochemical properties on the survival of Oncomelania hupensis snails, the intermediate host of Schistosoma japonicum.

Schistosoma japonicum, the causative agent of schistosomiasis, heavily relies on its single intermediate host, the Oncomelania hupensis snail, for its life cycle. Controlling these snails effectively plays a pivotal role in curbing the transmission and prevalence of this disease. While prior research has extensively investigated the impact of environmental factors such as temperature and vegetation on snail survival, growth, and reproduction, the contribution of water physicochemical properties has been notably underexplored. This study presents laboratory experiments designed to comprehensively explore the influence of water physicochemical properties on snail survival, offering valuable insights into environmental factors for more precise predictions of snail distribution. We meticulously conducted laboratory snail survival experiments using water from different sources (river water/tap water), and employed a statistical approach amalgamating principal component analysis with Cox regression to preliminarily investigate the effects of different water physicochemical properties on the survival of snails. Our analysis indicates that after a 6-month laboratory snail survival experiment, the survival rate in the tap water group was significantly higher than that in the river water group for infected snails (χ2 = 7.74, p = 0.005), while the difference in survival rates for non-infected snails was not statistically significant (χ2 = 0.61, p = 0.434). The Principal Component-Cox regression analysis revealed that in the infected snail group, total phosphorus, pH value, five-day biochemical oxygen demand, conductivity, and nitrite were protective factors for snail survival, while phosphate and total nitrogen were risk factors. In the non-infected snail group, total phosphorus, pH value, five-day biochemical oxygen demand, conductivity, and nitrite were protective factors for snail survival, and total nitrogen, ammonia nitrogen, phosphate, and nitrate were risk factors. This study underscores the substantial impact of water quality's physicochemical properties on snail survival. The effects of water quality on snails are complex, and maintaining an appropriate level of organic matter content and controlling the pH value at a weak alkalescency level prove beneficial for snail survival. These findings hold significant promise for advancing our understanding of snail-borne diseases and optimizing control strategies.

Handling Domain Drift and Unknown Fault Detection in Rotating Machinery Using Few‐Shot Learning With Data Scaling

ABSTRACTThis article presents a novel approach for fault diagnosis in rotating machinery using the few‐shot learning‐based unknown recognition and classification (FSLB‐UR&C) model. The core contribution of this research lies in addressing the challenges of domain drift, unknown fault detection, and uneven sampling intervals. The model effectively incorporates data scaling using Min–Max scaling to manage vibration data drift without altering the source data distribution, significantly extending the classification range. Principal component analysis visualizations substantiate the superior performance of Min–Max scaling over standard methods in processing drifted data, thus enhancing model accuracy. We validated the model on an in‐house bearing simulator and a coal conveyor motor, demonstrating its enhanced ability to classify drifted data and identify previously unknown faults. Additionally, time interpolation was applied to handle irregular sampling intervals, further optimizing the framework's robustness. Compared to other deep learning techniques, the FSLB‐UR&C model with data scaling exhibits markedly improved performance.

Distinct causes underlie double-peaked trilobite morphological disparity in cephalic shape.

Trilobite cephalic shape disparity varied through geological time and was integral to their ecological niche diversity, and so is widely used for taxonomic assignments. To fully appreciate trilobite cephalic evolution, we must understand how this disparity varies and the factors responsible. We explore trilobite cephalic disparity using a dataset of 983 cephalon outlines of c. 520 species, analysing the associations between cephalic morphometry and taxonomic assignment and geological Period. Elliptical Fourier transformation visualised as a Principal Components Analysis suggests significant differences in morphospace occupation and in disparity measures between the groups. Cephalic shape disparity peaks in the Ordovician and Devonian. The Cambrian-Ordovician expansion of morphospace occupation reflects radiations to new niches, with all trilobite orders established by the late Ordovician. In comparison, the Silurian-Devonian expansion seems solely a result of within-niche diversification. Linear Discriminant Analyses cross-validation, average cephalon shapes, and centroid distances demonstrate that, except for Harpida and the Cambrian and Ordovician Periods, order and geological Period cannot be robustly predicted for an unknown trilobite. Further, k-means clustering analyses suggest the total dataset naturally subdivides into only seven groups that do not correspond with taxonomy, though k-means clusters do decrease in number through the Palaeozoic, aligning with findings of decreasing disparity.

Near-optimal quantum kernel principal component analysis

Abstract Kernel principal component analysis (kernel PCA) is a nonlinear dimensionality reduction technique that employs kernel functions to map data into a high-dimensional feature space, thereby extending the applicability of linear PCA to nonlinear data and facilitating the extraction of informative principal components. However, kernel PCA necessitates the manipulation of large-scale matrices, leading to high computational complexity and posing challenges for efficient implementation in big data environments. Quantum computing has recently been integrated with kernel methods in machine learning, enabling effective analysis of input data within intractable feature spaces. Although existing quantum kernel PCA proposals promise exponential speedups, they impose stringent requirements on quantum hardware that are challenging to fulfill. In this work, we propose a quantum algorithm for kernel PCA by establishing a connection between quantum kernel methods and block encoding, thereby diagonalizing the centralized kernel matrix on a quantum computer. The query complexity is logarithmic with respect to the size of the data vector, $D$, and linear with respect to the size of the dataset. An exponential speedup could be achieved when the dataset consists of a few high-dimensional vectors, wherein the dataset size is polynomial in $\log(D)$, with $D$ being significantly large. In contrast to existing work, our algorithm enhances the efficiency of quantum kernel PCA and reduces the requirements for quantum hardware. Furthermore, we have also demonstrated that the algorithm based on block encoding matches the lower bound of query complexity, indicating that our algorithm is nearly optimal. Our research has laid down new pathways for developing quantum machine learning algorithms aimed at addressing tangible real-world problems and demonstrating quantum advantages within machine learning.

Mitigating excessive heat in Arabica coffee using nanosilicon and seaweed extract to enhance element homeostasis and photosynthetic recovery.

Global warming-related temperature increases have a substantial effect on plant and human health. The Arabica coffee plant is susceptible to growing in many places across the world where temperatures are rising. This study examines how nanosilicon and seaweed extracts can improve Arabica coffee plant resilience during heat stress treatment (49.0 ± 0.3°C) by maintaining mineral homeostasis and photosynthetic ability upon recovery. The principal component analysis arrangement of four treatments, nanosilicon (Si), seaweed extract (SWE), Si + SWE, and control (CT), showed each element ratio of magnesium, phosphorus, chloride, potassium, manganese, iron, copper, and zinc per silicon in ambient temperature and heat stress that found influenced upper shoot rather than basal shoot and root within 74.4% of largest feasible variance as first principal component. Magnesium and iron were clustered within the silicon group, with magnesium dominating and leading to a significant increase (p ≤ 0.05) in magnesium-to-silicon ratio in the upper shoot under heat conditions, especially in Si and Si + SWE treated plants (1.11 and 1.29 fold over SWE treated plant, respectively). The SWE and Si + SWE treated plants preserved chlorophyll content (15.01% and 28.67% over Si-treated plant, respectively) under heat stress, while the Si and Si + SWE treated plants restored photosynthetic efficiency (Fv/Fm) better than the SWE treated plant. The concomitant of the Si + SWE treatment synergistically protected photosynthetic pigments and Fv/Fm by adjusting the magnesium-silicon homeostasis perspective in Arabica coffee to protect real-world agricultural practices and coffee cultivation under climate change scenarios.

Investigating the effect of non-resonant background variation on the CARS data analysis of bacteria samples and classification using machine learning

Non-resonant background (NRB) plays a significant role in coherent anti-Stokes Raman scattering (CARS) spectroscopic applications. All the recent works primarily focused on removing the NRB using different deep learning methods, and only one study explored the effect of NRB. Hence, in this work, we systematically investigated the impact of NRB variation on Raman signal retrieval. The NRB is simulated as a linear function with different strengths relative to the resonant Raman signal, and the variance also changes for each NRB strength. The resonant part of nonlinear susceptibility is extracted from real experimental Raman data; hence, the simulated CARS data better approximate the experimental CARS spectra. Then, the corresponding Raman signal is retrieved by four different methods: maximum entropy method (MEM), Kramers-Kronig (KK), convolutional neural network (CNN), and long short-term memory (LSTM) network. Pearson correlation measurements and principal component analysis combined with linear discriminant analysis modeling revealed that MEM and KK methods have an edge over LSTM and CNN for higher NRB strengths. It is also demonstrated that normalizing the input data favors LSTM and CNN predictions. In contrast, background removal from the predictions significantly influenced Pearson correlation but not the classification accuracies for MEM and KK. Further, the LSTM performance is found to be limited and can only be applied for low NRB strengths. This comprehensive study has the potential to impact the CARS spectroscopy and microscopy applications in different areas.

Molecular characteristics of dissolved organic matter in the porewater of "Challenger" Deep sediments, Mariana Trench

Hadal trenches (> 6,000 m water depth) have been revealed as hotspots of organic carbon deposition and mineralization. Here, we present the molecular compositions of porewater dissolved organic matter (DOM) at the “Challenger” Deep (Site MT02; 10,954 m water depth) sediments of the Mariana Trench and the adjacent abyssal plain sediments (Site MT04; 5,800 m water depth) using ultra high-resolution Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS). The "Challenger" Deep sediments are characterized by higher abundance of recalcitrant compounds, mainly composed of highly unsaturated compounds (79.7%) and carboxyl-rich alicyclic molecules (57.1%), compared to the abyssal plain sediments (68.3%&51.5%). Principal component analysis suggests that TOC content in the sediments exerts an important control on the molecular characteristics of porewater DOM. It is likely that higher TOC content triggers elevated microbial-mediated organic matter mineralization, thereby forming more refractory organic matter compounds. These results improved the knowledge of the poorly-understood DOM compositions and microbial organic matter degradation in the ultra-deep, extreme ocean environment.

Data‐sparse price indexes by spatio‐temporal regularization and PCA: An application to the Australian housing market

AbstractWe present two novel approaches to overcome the limitations of data‐sparse local house price indexes and combine them into a single model pipeline that is simple, computationally efficient, and interpretable. The first contribution is a new spatio‐temporal regularization of least squares dummy variable models, such as the repeat sales regression used here. This regularization encodes prior knowledge of the proximity of houses in space and their sales in time. It handles missing values in a natural way. The second is nonlocal regularization using truncated principal component analysis (PCA) applied to the resulting national collection of local price indexes. The PCA loadings show that there are important underlying socioeconomic factors that can be leveraged in the construction of Australian market indexes. This PCA reveals important socioeconomic factors, showing that many local markets can be described by a few broad aspects of the national market, consisting of a general trend that contrasts regions influenced by the mining industry with Sydney and Melbourne, and another trend that highlights lifestyle.

Pregnancy detection based on blood serum sample Raman spectroscopy.

In this research, women were diagnosed as pregnant based on blood serum samples Raman spectroscopy. Raman spectroscopy is a vibrational technique that provides information on the chemical composition of samples. The Raman techniques have significantly impacted the study of various degenerative diseases, particularly cancer detection, using less invasive methods such as the analysis of blood serum samples. Additionally, these techniques have been used to study the health status of patients, which is often difficult to monitor using conventional techniques. This study obtained serum samples from 12 women diagnosed as pregnant and 11 non-pregnant volunteers (controls). Spectra were collected using a LabRAM HR800 Raman Spectrometer (Horiba Jobin-Yvon) with an 830 nm wavelength laser. For each serum sample, 10 Raman spectra were obtained by focusing the laser, using a microscope objective, on different points of the sample with an exposure time of 40 seconds and an irradiation power of 17 mW. The raw spectra were processed using baseline correction and smoothing to remove noise, fluorescence, and shot noise. Subsequently, the spectra were normalized and analyzed using the multivariate statistical method of Principal Component Analysis (PCA). In these spectra, the characteristic bands of main blood serum biomolecules such as phenylalanine (Phe), tyrosine (Tyr), glutathione, amide III, phospholipid, carotene, and tryptophan (Trp) can be observed. Nevertheless, when analyzing the average spectra of pregnant and non-pregnant women, the main spectral differences were associated with variations in molecules like glutathione, tryptophan (Trp), tyrosine, and phenylalanine, which occur during the first trimesters of pregnancy. This aligns with findings previously reported by other studies. Furthermore, the serum samples from pregnant and non-pregnant patients can be effectively discriminated using PCA applied to the Raman spectra, revealing two distinct clusters in the PCA plot corresponding to each group's status. The results demonstrate that pregnancy can be determined based on blood serum samples Raman spectroscopy with sensitivity and specificity. Although there are very effective devices on the market to determine pregnancy based on the Human Chorionic Gonadotropin (HCG) hormone detection in urine samples, these preliminary results indicate an alternative method known as Raman spectroscopy. On the other hand, the results could also suggest the possibility of carrying out other gynecological or fetal-related studies in women using these Raman techniques.

Evaluation of serum protein electrophoresis and immunofixation in dogs seropositive for various vector-borne pathogens.

Canine vector-borne diseases (VBDs) induce non-specific dysproteinemias, detectable by serum protein electrophoresis (SPE). VBDs have been reported to induce a monoclonal gammopathy pattern. Monoclonal gammopathies are commonly the result of paraprotein (M-protein) produced by an immunoglobulin-secreting neoplasm. The aims of this study were to characterize and compare SPE and immunofixation (IF) changes, evaluate the performance of previously identified SPE and IF interpretative criteria, and identify M-proteins in a cohort of dogs seropositive for a VBD and with an unknown history for an immunoglobulin-secreting neoplasm. A total of 143 serum samples from dogs that tested seropositive for different vector-borne pathogens were assessed by SPE. Cases with abnormal globulin fractions were further characterized by IF. Protein fraction and IF labeling results were evaluated using Kruskal-Wallis with Dunn's multiple comparisons and principal component analysis (PCA). IF was performed in 112 VBD-seropositive samples with dysproteinemia evaluated by SPE. Most (84/112, 75%) had a polyclonal expansion. Only two dogs had findings suggestive of an M-protein when considering both SPE and immunofixation. PCA clustered E.canis/A.phagocytophilum and B.gibsoni/CM.haematoparvum groups with relatively more γ-globulins than albumin and α-globulins, and the B.gibsoni/CM.haematoparvum group with more prominent IgA and IgM labeling than IgG labeling. Additionally, D.immitis clustered with more prominent β-globulins than γ-globulins and more IgG4 than IgG-FC. The previously derived interpretative criteria suggested an M-protein in very few VBD-seropositive dogs. PCA identified SPE and immunofixation pattern differences between dogs seropositive for different infectious agents.

Measuring subjective cognitive complaints with covid-19 brain fog using the subjective scale to investigate cognition (SSTICS)

The term “brain fog” has emerged from the observations of neuropsychiatric conditions present in post-COVID-19 infections. This is characterized by concentration and memory problems, selective attention disorders and difficulties in executive functions, yet it is unclear how long these deficits may persist and which cognitive functions are most vulnerable. Therefore, there is a need to properly evaluate these cognitive complaints using an assessment tool that specifies their intensity and nature. Our primary objective was to explore subjective perceptions of cognitive functioning in COVID-19-associated with brain fog using a tool that was previously validated for assessing subjective cognitive complaints. A total of 68 participants were recruited and the Subjective Scale to Investigate Cognition (SSTICS) was used to assess cognitive complaints. This was the first time that the SSTICS was used for this purpose in subjects with COVID-19. In addition, participants were administered a questionnaire assessing for the presence of various symptoms, as well as COVID-19 clinical parameters. The neuropsychological basis for the construct of the SSTICS was related to the cognitive complaints expressed by participants. A reliability analysis of our sample indicated a high degree of internal consistency (Cronbach’s alpha= 0.951). Associations between various SSTICS scores and COVID-related symptomatology and the differences between group of participants who reported cognitive complaints (“complainers”) and those who did not were assessed. We performed an exploratory factorial analysis based on Principal Component Analysis (PCA). Based on their distribution, participants were grouped into: “good functioning” - scores 0-9 (35.3%); “medium functioning” - scores 14-23 (25%); and “poor functioning” - scores 26-71 (39.7%). The mean SSTICS score was 20.59 (SD 16.61) and correlated with the quarantine duration and loss of smell. Complainers differed significantly from non-complainers in the total number of symptoms, the quarantine duration and the presence/absence of specific symptoms, such as loss of smell, tiredness and aches/pains. Our study showed that >10% of patients reported subjective cognitive complaints following COVID-19, with most reporting mild or serious cognitive complaints, mostly within the domains of memory, attention, language, executive functioning or praxis.

Morphological and Optical Profiling of Melanocytes and SK-MEL-28 Melanoma Cells Via Digital Holographic Microscopy and Quantitative Phase Imaging.

Melanoma, which originates from pigment-producing melanocytes, is an aggressive and deadly skin cancer. Despite extensive research, its mechanisms of progression and metastasis remain unclear. This study uses quantitative phase imaging via digital holographic microscopy, Principal Component Analysis (PCA), and t-distributed Stochastic Neighbor Embedding (t-SNE) to identify the morphological, optical, and behavioral differences between normal melanocytes and SK-MEL-28 melanoma cells. Our findings reveal significant differences in cell shape, size, and internal organization, with SK-MEL-28 cells displaying greater size variability, more polygonal shapes, and higher optical thickness. Phase shift parameters and surface roughness analyses underscore melanoma cells' uniform and predictable textures. Violin plots highlight the dynamic and varied migration of SK-MEL-28 cells, contrasting with the localized movement of melanocytes. Hierarchical clustering of correlation matrices provides further insights into complex morphological and optical relationships. Integrating label-free imaging with robust analytical methods enhances understanding of melanoma's aggressive behavior, supporting targeted therapies and highlighting potential biomarkers for precise melanoma diagnostics and treatment.

Genetic Diversity and Genome-Wide Association Study of Pleurotus pulmonarius Germplasm

Pleurotus pulmonarius is prized by consumers for its distinct flavor, strong aroma, and dense, crispy texture. Although China has extensive germplasm resources for P. pulmonarius, only a limited number of cultivars are commercially available. A comprehensive evaluation and detailed analysis of P. pulmonarius germplasm, alongside the exploration of superior germplasm resources, are essential for developing new varieties. In this study, we resequenced the genomes of 47 P. pulmonarius strains collected nationwide, identifying a total of 4,430,948 single nucleotide polymorphism (SNP) loci. After filtering based on minor allele frequency and data integrity, 181,731 high-quality SNP markers were retained. Phylogenetic analysis grouped the strains into six clusters, with strains from similar geographical regions clustering together. Most CBS strains formed a single cluster; cultivated varieties exhibited higher genetic similarity, whereas wild strains displayed greater diversity. Principal component analysis (PCA) and population structure analyses, using the same SNP markers, corroborated the phylogenetic findings. DNA fingerprinting, derived from 369 core SNPs, further underscored the genetic diversity among strains. Significant morphological variation was observed, with strains in groups ZP, CBS, and WHLJ exhibiting notably higher yields and cap widths compared to other groups. Correlation analysis revealed associations among various phenotypes, while genome-wide association study (GWAS) identified multiple SNP markers within candidate genes linked to agronomic traits, most of which were controlled by multiple genes. This research offers a molecular-level characterization and evaluation of P. pulmonarius germplasm resources, providing a scientific basis for enriching available germplasm and advancing breeding materials.

Deciphering Temporal Metabolome Dynamics in Response to MYMV: Contrasting Patterns in Resistant and Susceptible Blackgram (Vigna mungo L. Hepper) Cultivars.

Blackgram (Vigna mungo L. Hepper) production is hindered by mungbean yellow mosaic virus (MYMV) with disease incidence up to 85% in hot spot locations of Tamil Nadu, India. Field screening of 50 genotypes identified Mash 114 as resistant and CO 5 as susceptible cultivars. To understand the resistance mechanism, temporal metabolome variations of resistant (RC) and susceptible (SC) cultivars were assessed at 3, 6, and 12 days post-inoculation with MYMV via whitefly transmission using GC-MS. The study included aviruliferous whitefly and healthy controls. Data analysis identified 98 differential compounds across various metabolic groups. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed distinct metabolic profiles, with RC accumulating antiviral terpenoids such as ursolic acid, betulin, and umbelliprenin, while SC upregulated lipids and fatty acids favoring virus replication. These findings provide insights for breeders to identify resistant sources and pave the way for improved MYMV management strategies using antiviral products.

Identification of Primary Metabolite Profiles Reveals Quality Characteristics of Citrus maxima ‘Shatian Yu’ from Different Origins

Citrus maxima ‘Shatian Yu’ displays varying nutritional profiles influenced by its production area. This study evaluated pomelo fruits from four primary and one developing ‘Shatian Yu’ production area. Notably, ‘Shatian Yu’ from the Guilin area exhibited higher sugar and alcohol content, suggesting enhanced taste. Principal component analysis and OPLS-DA revealed significant metabolite differences among production areas. In Guilin, variations were observed in a few substances, including sugars, alcohols, and phenolic acids. When compared with Rong City, Guilin showed a decrease in four phenolic acids and an increase in three organic acids, eighteen amino acids, eighteen lipids, and one vitamin. This comprehensive analysis provides valuable insights for the development of ‘Shatian Yu’ cultivation, highlighting the impact of production areas on fruit quality.

PATH-36. DO GENE EXPRESSION PROFILES BETTER PREDICT OLIGODENDROGLIOMA SURVIVAL?

Abstract The WHO 2021 classification of oligodendroglioma grade 2 (G2) and grade 3 (G3), is separated by histopathology alone and does not universally reflect survival outcome. DNA methylation-based profiling also fails to separate oligodendroglioma grades. Our aim was to define transcriptome-based molecular subgroups that predict survival of oligodendroglioma (IDH-mutant and 1p/19q-codeleted) patients. Clinical and whole-transcriptome data for oligodendroglioma were obtained from TCGA (n=132), CGGA (n=53), and the Exsegen Genomics Research Biobank (n=16) (CTRI/2021/09/036861); while that of control brain samples (n=9) were obtained from CPTAC. Overall survival (OS) was significantly different in TCGA samples between WHO CNS G2 and G3 oligodendrogliomas (log-rank test, p=0.0001); but, not in CGGA (p=0.1833) or Exsegen (p>0.06) samples. Genes with highly variable expression (Median Absolute Deviation, i.e. MAD>1; 1645 genes) across TCGA oligodendroglioma and CPTAC control brain samples were used to exclude 9 oligodendroglioma samples with Pearson’s correlation>0.75 with control brain samples. Consensus clustering (K-means and spectral) of remaining 124 TCGA oligodendroglioma samples identified three significantly different (SigClust, p<0.05) transcriptomic classes using 1347 highly variable genes (MAD>1) in these samples. Principal component analysis (PCA) on expression of 1347 genes in TCGA samples revealed partial overlap of G2 and G3 tumours; but transcriptomic classes with distinct OS could be identified at both extremes of survival. Gene expression signatures for each transcriptomic class were developed using core TCGA samples from each class. For further validation, gene signatures were used to predict the transcriptomic classes of CGGA and Exsegen samples based on highest cosine similarity between sample gene expression and gene signature of each transcriptomic class. OS was significantly different between the transcriptomic classes in TCGA samples and predicted ones in CGGA samples. Transcriptome profiling separated oligodendrogliomas into distinct classes which can predict survival and potentially augment grading and refine prognostication and treatment options.

Celiac disease care differs significantly between high- and low-middle-income countries: a global survey of celiac experts from 63 countries.

Celiac disease (CeD) is increasingly diagnosed but significant disparities exist in awareness, practices, resources, and legislation worldwide. We conducted a global online survey with CeD experts to assess this disparity internationally. A 55 questions survey encompassing nine domains relevant to CeD care (awareness, gluten-free [GF] foods availability/cost/quality, GF labeling, CeD dietician availability, insurance for CeD patients, medical training, research funding, patient support groups, and unmet needs) was generated and sent to CeD experts worldwide electronically. Countries were stratified based on per capita income as high-income (HIC) and lower-income countries (LIC) (including upper-middle-, lower-middle-, and low-income countries). Survey responses were summarized as a single score using principal component analysis. Valid responses were obtained from 131(37.4%) [HIC: 71; LIC: 60] of contacted CeD experts from 63 countries. Compared with HIC, LIC experts perceived worse availability (HIC:80% vs LIC: 47%; P<0.001), quality (52% vs 20%; P<0.001), and legislation for labeling of GF foods (82% vs 37%; P<0.001), with unfavorable reimbursement policies (27% vs 12%; P=0.002), subsidies (32% vs 13%; P<0.001), and insurance (76% vs 43%; P<0.001) for CeD patients. LIC also lacked awareness about CeD among general physicians (69% vs 32%; P<0.001), trained celiac dieticians (39% vs 12%; P=0.002), and active CeD patient support groups (93% vs 50%; P<0.001). All experts believed that GF foods were costly (94% vs 87%), frequently contaminated (27% vs 32%), and unfavorably taxed (97% and 93%). The experts agreed on key unmet needs and better research funding. Overall CeD preparedness score (median 58.3 vs 33.0; P<0.001) was also associated with income. The present survey highlights the opinion of global experts on the challenges, opportunities, and preparedness related to CeD and differences worldwide by income.

Optical coherence tomography characteristics in hydroxychloroquine retinopathy and the correlations with visual deterioration in Taiwanese

Abstract PURPOSE: This study aimed to investigate optical coherence tomography (OCT) characteristics in hydroxychloroquine (HCQ) retinopathy and their correlation with visual acuity among Taiwanese patients. MATERIALS AND METHODS: We retrospectively recruited patients undergoing long-term HCQ treatment who had received examinations of best-corrected visual acuity and OCT scans. We observed disruptions in the ellipsoid zone (EZ) and retinal pigment epithelium (RPE) across different retinal regions. Principal component analysis (PCA) was employed to identify the most significant factors associated with visual deterioration. RESULTS: Among the 120 eyes included in the study, HCQ retinopathy was present in 42 eyes (35.0%). In patients with mild-to-moderate retinopathy, the pericentral pattern was predominant (75.0%), whereas no parafoveal pattern was observed. Serial examinations revealed that lesions typically progressed from pericentral to parafoveal and foveal regions. EZ disruption was observed in all affected cases, most frequently at the pericentral region (100%), followed by the perifoveal (87.4%), parafoveal (72.1%), and foveal (43.2%) regions. RPE disruption was noted in 59.5% of cases, with the highest prevalence at the pericentral (53.2%) and perifoveal (52.3%) regions, followed by the parafoveal (33.3%) and foveal (28.8%) regions. PCA identified RPE disruption at the fovea and parafoveal regions as the most strongly correlated factors for visual deterioration. CONCLUSIONS: In Taiwanese patients, HCQ retinopathy predominantly manifests with pericentral lesions, while isolated parafoveal lesions are rare as an initial presentation. RPE disruption, rather than EZ disruption, appears to be the primary determinant for visual deterioration in this population.

COVID-19 inflammatory signature in a Mozambican cohort: unchanged red blood series and reduced levels of IL-6 and other proinflammatory cytokines.

Alterations in haematological, biochemical parameters and cytokine levels, were reported in patients with COVID-19, however, there is an underrepresentation of the African population, which could provide evidence for understanding SARS-CoV-2 pathogenesis and useful tools for clinical management of cases. In this study, we aimed to determine the haematological, biochemical and cytokine profile in Mozambican individuals with SARS-CoV-2. A cohort of 85 Mozambican individuals with RT-PCR SARS-CoV-2 results, was stratified into negative, asymptomatic, mild, moderate, and severe categories. Haematological, biochemical and cytokines measurement were performed on samples from the study participants. Principal component analysis (PCA) was performed to identify similar patterns among the study cases. Comparisons between groups were performed using the Kruskal-Wallis test. Receiver operating characteristic (ROC) and area under the curve (AUC) analysis were conducted to evaluate the ability of these parameters to distinguish severe from non-severe cases of SARS-CoV-2 infection. SARS-CoV-2 infection was associated with a significant (p < 0.05) decrease in peripheral blood absolute counts of total lymphocytes and eosinophils, below the reference values along with no abnormal change (p > 0.05) in red blood cell count, haemoglobin, platelets and other red series parameters. At the serum level, SARS-CoV-2 infection was associated with an increase in serum levels of C-reactive protein (C-RP) and glucose above the reference values and to a significant reduction a significant (p < 0.05) reduction in levels of interferon-gamma (INF-γ), Tumour Necrosis Factor alfa (TNF-α) and the interleukin 1 beta (IL-1β) and IL-6 in severe cases, when compared to negative cases. Haematological, biochemical and cytokine profiles segregate severe from non-severe cases of COVID-19 with an excellent performance of C-RP (AUC = 0.95; p < 0.001) and good performance of lymphocytes (AUC = 0.88; p < 0.001) and IL-15 (AUC = 0.86; p < 0.001). The lack of variation in red and platelet series, coupled with a decrease in the levels of classical pro-inflammatory in severe cases, deviates from what has been reported in other contexts suggesting, that there may be peculiarities in COVID-19 manifestation within the context of this study population. Furthermore, these results identify parameters with potential for clinical management of COVID-19 and therefore good resource allocation, particularly for severe cases.

Unravelling the detoxification trail of potential toxic heavy metals: an insight into heavy metal auditing and ecological health upon valorisation by Lampito mauritii and Eudrilus eugeniae.

Evidence on prospective remediation of municipal solid waste contaminated with toxic heavy metals by Eudrilus eugeniae (Eu) and Lampito mauritii (L) is very scarce and yet to be explored. In this study, heavy metal detoxification potential of E. eugeniae and L. mauritii in municipal solid waste (MSW) + cowdung (CD) (3:1)-based feedstocks were investigated against Eisenia fetida (E) (a well-known vermi-remediator) and aerobic composting. Excellent reduction (70.01-93.04%) of potentially toxic heavy metals (PTHMs) (Pb, Cr, Cd and Zn) were evident in both E. eugeniae and L. mauritii employed treatments. Moreover, the results on heavy metal budget quotient clearly demonstrated the unique detoxification route undertaken by E. eugeniae and L. mauritii via humic composite facilitated chelation over the nominal bioaccumulation pathway. The principal component analysis (PCA) confirmed the strong negative correlation between the heavy metal (HM) level in earthworm gut and MSW substrate, whereas a strong positive correlation between humic substances and HM remediation. Furthermore, analysis of ecological health parameters indicated substantial reduction of environmental risk and guaranteed negligible risk of PTHM if utilized as manure. Moreover, significant increment in total N content (3.2-3.8-fold), available P (4-5.9-fold), exchangeable K (3.66-fourfold) and enzyme activity along with significant reduction of TOC (~ 87%) confirmed E. eugeniae and L. mauritii could effectively stabilize MSW. Thus, the metal-binding potential of humic substances produced by earthworms during the detoxification of municipal solid waste (MSW), coupled with a metal budget analysis, has offered valuable insights into the usage of E. eugeniae and L. mauritii as effective contenders for sanitizing heavy metal-laden MSW.