Large Number Of Samples Research Articles

Optimization of the Extraction Protocol for Pacific Ciguatoxins from Marine Products Prior to Analysis Using the Neuroblastoma Cell-Based Assay

Ciguatera poisoning (CP) is caused by the consumption of marine products contaminated with ciguatoxins (CTXs) produced by dinoflagellates of the genus Gambierdiscus. Analytical methods for CTXs, involving the extraction/purification of trace quantities of CTXs from complex matrices, are numerous in the literature. However, little information on their effectiveness for nonpolar CTXs is available, yet these congeners, contributing to the risk of CP, are required for the establishment of effective food safety monitoring programs. An evaluation of six extraction/purification protocols, performed with CTX3C spiked on fish flesh and a neuroblastoma cell-based assay (CBA-N2a), revealed recoveries from 6 to 45%. This led to the development of an optimized 3-day protocol designed for a large number of samples, with CTX1B and CTX3C eluting in a single fraction and showing recoveries of 73% and 70%, respectively. In addition, a reduction in adverse matrix effects in the CBA-N2a analyses was demonstrated with naturally contaminated specimens, increasing the sensitivity of the method, which now meets the very low guidance level recommended by international agencies. However, efforts are still required to reduce the signal suppression observed in LC-MS/MS analysis. This optimized protocol contributes to the technological advancement of detection methods, promoting food safety and improving CP risk assessment in marine products.

InDrops-2: a flexible, versatile and cost-efficient droplet microfluidic approach for high-throughput scRNA-seq of fresh and preserved clinical samples.

The expansion of single-cell analytical techniques has empowered the exploration of diverse biological questions at the individual cells. Droplet-based single-cell RNA sequencing (scRNA-seq) methods have been particularly widely used due to their high-throughput capabilities and small reaction volumes. While commercial systems have contributed to the widespread adoption of droplet-based scRNA-seq, their relatively high cost limits the ability to profile large numbers of cells and samples. Moreover, as the scale of single-cell sequencing continues to expand, accommodating diverse workflows and cost-effective multi-biospecimen profiling becomes more critical. Herein, we present inDrops-2, an open-source scRNA-seq technology designed to profile live or preserved cells with a sensitivity matching that of state-of-the-art commercial systems but at a 6-fold lower cost. We demonstrate the flexibility of inDrops-2, by implementing two prominent scRNA-seq protocols, based on exponential and linear amplification of barcoded-complementary DNA, and provide useful insights into the advantages and disadvantages inherent to each approach. We applied inDrops-2 to simultaneously profile multiple human lung carcinoma samples that had been subjected to cell preservation, long-term storage and multiplexing to obtain a multiregional cellular profile of the tumor microenvironment. The scalability, sensitivity and cost efficiency make inDrops-2 stand out among other droplet-based scRNA-seq methods, ideal for large-scale studies on rare cell molecular signatures.

A new approach for detecting HPV DNA in cervical swabs: comparison of nucleic acid extraction with direct PCR

BackgroundAlmost all cases of cervical cancer are associated with persistent high-risk HPV infection. WHO prioritizes primary HPV testing for cervical cancer screening. Cervical cancer screening programs require the ability to process a large number of samples in a simple and standardized manner and obtain reliable results. The workload, time, and cost associated with the number of samples to be processed encourage the development of alternative methods to the traditional nucleic acid extraction method for population-based screening. In this study, we aimed to compare the performance of a commercial pre-denaturation solution with nucleic acid extraction in HPV DNA detection by PCR in cervical swab samples.MethodsThe study was designed in two phases: an experimental phase and a clinical phase. A total of 1200 cervical swabs were included in the clinical phase of the study. Positive results were obtained in 143 (11.9%) samples by nucleic acid extraction and 137 (11.4%) samples by PharmaDirect. Discordant results were detected in 28 (2.3%) samples.ResultsPharmaDirect provided 88.1% sensitivity compared to nucleic acid extraction. PharmaDirect provided high sensitivity for HPV genotype 16 (92.3%) and relatively limited sensitivity for mixed genotype infections (73.7%).ConclusionThis study demonstrates the potential of an alternative commercial pre-denaturation product that does not require nucleic acid extraction for HPV DNA detection in cervical swab samples. Such approaches may represent a useful alternative for population-based screening studies.Trial registrationNot applicable.

Accurate identification of primary site in tumors of unknown origin (TUO) using DNA methylation

Tumors of unknown origin (TUO) generally result in poor patient survival and are clinically difficult to address. Identification of the site of origin in TUO patients is paramount to their improved treatment and survival but is difficult to obtain with current methods. Here, we develop a random forest machine learning TUO methylation classifier using a large number of primary and metastatic tumor samples. Our classifier achieves high accuracy in primary site identification when applied to both publicly available and internal validation samples, with 97% of samples classified correctly and 85% receiving high probability scores (≥0.9). Moreover, by employing pathologist expertise and t-SNE visualization, the TUO classifier can assign samples to 46 different sites of origin/disease classes. This strategy also revealed multiple classes of yet unknown significance for future exploration. Overall, the presented TUO classifier represents a significant step forward in the diagnosis of TUO tumors.

The Dutch Early-Stage Melanoma (D-ESMEL) study: a discovery set and validation cohort to predict the absolute risk of distant metastases in stage I/II cutaneous melanoma.

Early-stage cutaneous melanoma patients generally have a favorable prognosis, yet a significant proportion of metastatic melanoma cases arise from this group, highlighting the need for improved risk stratification using novel prognostic biomarkers. The Dutch Early-Stage Melanoma (D-ESMEL) study introduces a robust, population-based methodology to develop an absolute risk prediction model for stage I/II melanoma, incorporating clinical, imaging, and multi-omics data to identify patients at increased risk for distant metastases. Utilizing the Netherlands Cancer Registry and Dutch Nationwide Pathology Databank, we collected primary tumor samples from early-stage melanoma patients, with and without distant metastases during follow-up. Our study design includes a discovery set of metastatic cases and matched controls to identify novel prognostic factors, followed by a validation cohort using a nested case-control design to validate these factors and to build a risk prediction model. Tissue sections underwent Hematoxylin & Eosin (H&E) staining, RNA sequencing (RNAseq), DNA sequencing (DNAseq), immunohistochemistry (IHC), and multiplex immunofluorescence (MxIF).The discovery set included 442 primary melanoma samples (221 case-control sets), with 46% stage I and 54% stage II melanomas. The median time to distant metastasis was 3.4years, while controls had a median follow-up time of 9.8years. The validation cohort included 154 cases and 154 controls from a random population-based selection of 5,815 patients. Our approach enabled the collection of a large number of early-stage melanoma samples from population-based databases with extensive follow-up and a sufficient number of metastatic events. This methodology in prognostic cancer research holds the potential to impact clinical decision-making through absolute risk prediction.

Predicting fiber content in herbivore fecal samples using a multispecies NIRS model.

Fiber is essential for rumen health, microbial fermentation, and the energy supply of herbivores. Even though the study of fecal fiber contents (neutral detergent fiber NDF, acid detergent fiber ADF, and acid detergent lignin ADL) using near-infrared reflectance spectroscopy (NIRS) has allowed investigating nutritional ecology of different herbivore species, NIRS calibrations are species-specific and require a large number of samples for predictions. A multispecies calibration would be an advantage since samples from different herbivores could be used to calibrate a model capable of predicting the fecal fiber content of other herbivores. To date, however, multispecies models have not been developed to predict fiber contents in the feces of herbivores. Here, we fill this gap by calibrating three fiber multispecies models (NDF, ADF and ADL) using fecal samples from domestic and wild herbivore species. We also evaluated the effect of incorporating sodium sulfite in fiber determination protocol. The initial dataset consisting of 445 samples of six herbivore species was used to calibrate (80% of the samples) and validate (20% of the samples) the models. Subsequently, 63 samples of five herbivores not included in the calibration set were used for the external validation of the model. Since sodium sulfite did not significantly improve fecal fiber prediction, our model was developed without this compound. The multispecies models obtained were highly accurate determining NDF, ADF and ADL (R2CAL, coefficient of determination in calibration, ≥ 0.93, R2VAL, coefficient of determination in validation, ≥ 0.91) and independent of external confounders. For external validation, the accuracy in predicting fecal samples in other herbivore species was also satisfactory, with consistently better values for NDF (R2VAL, 0.86-0.94) and ADF (R2VAL, 0.80-0.95) than for ADL (R2VAL, 0.66-0.89). We show that multispecies NIRS calibrations can be used with high accuracy to assess fecal fiber contents across diverse herbivore species. This finding represents a significant advance in the study of the nutritional ecology of herbivores with contrasting foraging patterns. In the future, widening the data range (e.g., species and locations) of the initial dataset could further improve the accuracy of these models.

An optimization method for terahertz metamaterial absorber based on MOPSO

Metamaterials can freely control terahertz waves to obtain the desired electromagnetic characteristics by designing the geometry and direction of the unit structure, which is widely used in sensing, communication and stealth technology in radar. The traditional design of terahertz metamaterial absorber usually requires continuous structural adjustment and a large number of simulations to meet the expected requirements. The process is heavily dependent on the experience of researchers, and the physical modeling and simulation solution process is time-consuming and inefficient, which has greatly hindered the development of metamaterial absorbers. Therefore, deep learning has been used to predict the structural parameters or spectra of metamaterial absorbers due to its powerful learning ability. However, when designing a new structure, a large number of training samples need to be reprepared, which is time-consuming and not universal. Particle swarm optimization can quickly converge to the optimal solution through the sharing and cooperation of individual information in the group, without prior preparation. Therefore, this paper proposed a fast design method of terahertz metamaterial absorber based on multi-objective particle swarm optimization algorithm. Taking a new center symmetric absorber structure composed of four L as an example, the structure parameters are optimized to achieve fast automatic design of metamaterial absorber. The multi-objective particle swarm optimization takes the absorptivity and quality factor as independent targets to design the structure parameters of the absorber, realizes the dual-objective optimization of the absorber, and overcomes the shortcoming of the multi-objective conflict that PSO cannot solve. The optimally-designed absorber achieves perfect absorption at 1.613THz with a quality factor of up to 319.72 and a sensing sensitivity of 264.5GHz/RIU when used for refractive index sensing. In addition, the causes of absorption peaks are analyzed in detail using impedance matching, surface current, and electric field distribution. By studying the polarization characteristics of the absorber, it is found that it is not sensitive to polarization, which is more stable in practical application. In summary, the multi-objective particle swarm optimization algorithm can realize the design according to the demand, reduce the experience requirement of researchers in the design of metamaterial absorber, improve the design efficiency and performance, and have great application potential in the design of terahertz functional devices.

Investigating Gram-negative bacilli isolates’ sensitivity to ceftazidime/avibactam

ABSTRACT Background: Multidrug resistant (MDR) Gram negative organisms are becoming increasingly common. Carbapenem resistant Enterobacterales (CRE) pose a major threat and necessitate the development of new antibiotics. MDR and carbapenem resistant infections, which are common in intensive care units and hospitals, lead to increased morbidity, mortality, prolonged hospital stays, and higher healthcare costs. New antimicrobials such as ceftazidime avibactam offer potential alternatives to conventional treatments such as tigecycline and colistin, which have significant side effects and limitations. Aim: This study focuses on the antibiotic susceptibility of ceftazidime/ avibactam to Gram negative bacilli found in a large number of clinical samples collected from a tertiary care facility in Netaji Subhas Medical University and Hospital, Bihta, India. Methodology: The study included 81 Gram negative bacteria isolated from patient samples. Based on the Clinical Laboratory Standards Institute guidelines mentioned in the Kirby Bauer disc diffusion method. Result and Conclusion: the results showed that ceftazidime avibactam inhibited 89.9% of the Enterobacteriaceae isolates, which was higher than the 80.3% of amikacin and the 85.1% of meropenem. Ceftazidime avibactam was effective against CRE isolates in 69.9% of cases and against MDR isolates in urine in 94% of cases, which was higher than the 40% of ceftriaxone and 94% of nitrofurantoin. The results show that ceftazidime avibactam can cure MDR and CRE infections, especially urinary tract infections, better than conventional antibiotics, which is a great help in the fight against increasing antibiotic resistance.

Species-specific mercury speciation in billfishes and its implications for food safety monitoring and dietary advice.

Humans are exposed to toxic methylmercury mainly by consuming marine fish, in particular top predator species like billfishes or tunas. In seafood risk assessments, mercury is assumed to be mostly present as organic methylmercury in predatory fishes; yet high percentages of inorganic mercury were recently reported in marlins, suggesting markedly different methylmercury metabolism across species. We quantified total mercury and methylmercury concentrations in muscle of four billfish species from the Indian and the Pacific oceans to address this knowledge gap. We found low percentages of methylmercury in blue and black marlins (15±7%) compared to swordfish and striped marlin (89±13%), with no significant differences among ocean regions. This illustrates that billfishes exhibit species-specific methylmercury bioaccumulation patterns, likely related to unique selenium-dependent in vivo methylmercury demethylation capacities in muscle. Blue and black marlins therefore appeared generally safer for human consumption than swordfish and striped marlin regarding MeHg toxicological effects. Yet, no matter the species, the frequency of recommended weekly billfish meals decreased with increasing fish size, given that mercury naturally accumulates over time. When assessing potential risks of billfish consumption, we therefore recommend measuring methylmercury, rather than total mercury, and relying on a large number of samples to cover a broad range of fish sizes. This study calls for additional characterization of mercury speciation and bioavailability in billfishes to better understand the mechanisms driving species-specific differences of methylmercury detoxification, and to refine dietary advices associated to marine top predators consumption.

A novel sensor-independent convolutional neural network for structural damage detection：illustrated by a case study on gantry crane

Structural damage detection is a critical technology for ensuring the safety of engineering structures. In recent years, intelligent structural damage detection algorithms based on machine learning have garnered widespread attention globally, thanks to their efficient and reliable detection performance. However, existing damage detection methods largely depend on the use of sensors, which not only increases operational and maintenance costs but also limits the applicability of these methods—especially in parts of structures where sensors are difficult to install. To overcome this barrier, this paper introduces a novel sensor-independent convolutional neural network (CNN) approach for predicting the maximum response of structures under seismic excitation. This method utilizes IDA method to collect a large number of samples (these samples have been uploaded to Mendeley Data for reference). It employs innovative data processing and grouping methods for feature alignment as well as the division of the test and training sets. By automatically extracting the signal characteristics of seismic excitation and optimizing feature combinations through multi-layer fusion, this method achieves high-precision and robust prediction without relying on sensors. To validate the effectiveness of this method, this study conducted a performance evaluation on a typical gantry crane metal structure. The results show that the proposed sensor-independent CNN algorithm achieves high-precision structural response prediction, with over 99% accuracy for peak responses, demonstrating the robustness and reliability of the model. It also performs excellently in key metrics such as root mean square error, error bias, and learning rate progression, making it a reliable and cost-effective solution for real-time seismic damage detection.

Characterizing Nonalcoholic Fatty Liver Disease (NAFLD) in Lean Individuals at a Tertiary Care Hospital: A Cross-sectional Study.

Fat accumulation in the liver is affecting 38% of the global population. It can also occur in normal-weight individuals, termed lean non-alcoholic fatty liver disease (NAFLD). This study examines Asian and Western body mass index (BMI) criteria, as well as metabolic dysfunction-associated fatty liver disease (MAFLD) and metabolic dysfunction-associated steatotic liver disease (MASLD) diagnostic guidelines, in lean fatty liver cases within a healthcare setting. This study was cross-sectional included 111 lean patients diagnosed with NAFLD using either ultrasound or VCTE from January 2023 to March 2024. Anthropometric, laboratory and non-invasive liver fibrosis evaluation parameters were used. The study assessed clinical characteristics and metabolic risk factors of patients with BMI ≤ 23 kg/m2 and BMI between 23 and ≤ 25 kg/m2 using MASLD and MAFLD diagnostic criteria. The cohort included NAFLD patients with a mean age of 43.3 years (±13.2 years). Of the participants, 33% were diagnosed through ultrasonography, whereas 67% diagnosis were made via Fibro scan. Majority were male 92 (83%), while females were 19 (17%) of the entire group. The lean NAFLD criteria for Asia and the West were satisfied by 43 (39%) persons with a BMI ≤ 23 kg/m2 and 68 (61%) individuals with a BMI between 23 and ≤ 25 kg/m2, respectively. The average body mass index (BMI) was 23.0 ± 1.5 kg/m2. Diabetes was observed in 16%, hypertension 11%, and ischemic heart disease in 2%. Out of the total individuals, 92 satisfied the MASLD-MAFLD criteria, whereas 18 did not qualify the MAFLD criteria for diagnosis and were classed as MASLD-Alone. Elevated triglycerides, insulin resistance (HOMA-IR ≥ 2), and three or more cardiometabolic risk factors (CMRF) were significant in the MASLD-MAFLD group compared to the MASLD-Alone group (p < 0.05). Comparing BMI criteria, no significant differences were found in terms of fibrosis between the Western and Asian lean NAFLD BMI criteria's (p = 0.243). Lean NAFLD is a major global health concern. Applying non-Asian BMI criteria (BMI ≤ 25 kg/m2) for lean Asians improves early detection and intervention for at-risk individuals. Accurate use of MAFLD and MASLD criteria is essential to prevent confusion in diagnosing lean NAFLD. Further multicenter investigations with larger sample numbers are required to corroborate these results in our community. Nazir S, Abbas Z, Gazder DP, et al. Characterizing Nonalcoholic Fatty Liver Disease (NAFLD) in Lean Individuals at a Tertiary Care Hospital: A Cross-sectional Study. Euroasian J Hepato-Gastroenterol 2024;14(2):198-204.

Bioinformatics-based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy response

Lung squamous cell carcinoma (LUSC) is a subtype of non-small cell lung cancer. It has a grim prognosis for patients, primarily because the disease often remains asymptomatic in its early stages. As a result, it is frequently diagnosed at an advanced stage, limiting treatment options. This underscores the importance of studying potential biomarkers and developing personalized treatment strategies. In this study, we used an advanced bioinformatics approach, integrating two authoritative databases, NCBI's GEO and TCGA, to perform a large-scale cross-platform gene expression analysis. To deeply mine the gene expression data of a large number of lung squamous carcinoma samples, we used a screening strategy based on median absolute deviation to select genes that differed significantly in multiple datasets. The expression variations of these genes between normal and cancerous tissues provided us with valuable clues revealing key molecules that may be involved in the disease process. Through rigorous statistical tests, we identified 36 genes that were significantly associated with patient survival, and further constructed a model using Cox proportional risk model containing 11 key genes (MRPL40, GABPB1AS1, PTPN3, SNCA, PYGB, RAP1, VDR, PHPT1, KIAA0100, TBC1D30, CYP7B1) in a risk prediction model. The prediction model not only reflects the strong correlation between gene expression and LUSC prognosis, but also provides clinicians with an effective tool to predict patients' survival prospects. In the future, this model is expected to guide the development of individualized treatment plans, thereby improving the quality of life and overall prognosis of patients.

IDENTIFICATION OF CLINICALLY RELEVANT GENE VARIANTS IN COLON ADENOCARCINOMA SAMPLES OF UKRAINIAN PATIENTS USING A COMPREHENSIVE CANCER PANEL: A PILOT STUDY.

We have studied 20 samples of Ukrainian patients with colorectal adenocarcinomas of various differentiation grades. To identify the clinically relevant gene variants, the CCP data were filtered using the Franklin by Genoox database. A total of 79 clinically relevant gene variant alterations (SNVs, INDELs) were found in 28 of 409 genes. The largest number of mutations was found in 3 genes, APC, TP53, and KRAS (16, 14, and 8, accordingly). We revealed 4 variants in PTEN and SMAD4, 3 variants in CHEK2, ERBB2, and PIK3CA genes, and 2 variants in AKT1, ATM, DST, IDH1, and TCF12. Mutations for 7 genes, KRAS, TP53, CHEK2, PTEN, AKT1, APC, and SMAD4, were found in more than 1 tumor tissue sample. Tier 1-2 gene variants rate was about 50% of all genetic variants. The therapeutic significance was found in more than 55% of mutations. Additionally, 11 novel genetic mutations in 9 genes have been identified, including G6PD, APC, DST, SINE1, SMAD2, and FLCN. These data suggest a high level of clinical relevance of the NGS CCP approach. Further confirmation on a larger number of samples and using a deeper analysis by other approaches is required.

Pair-Soil-Spectra: An Approach for NIRS-Based Soil Total Nitrogen Content Detection with Feature Metrics in Cases of Small Sample Sizes.

Soil total nitrogen (STN) plays an important role in plant growth, and rapid and nondestructive detection of STN content is essential for agricultural production. Near-infrared spectroscopy (NIRS) takes advantage of the fast detection speed, low cost, and nondestructiveness, and it can be used for STN content detection. Typically, NIRS-based approaches require a large number of samples for detection model training. However, it is difficult to collect sufficient samples due to various causes (e.g., time-varying state, high assay costs, etc.) in practical application. To tackle this problem, a feature metric approach is introduced to detect the STN content based on NIRS in this work, and a new approach (named Pair-Soil-Spectra) is proposed to mine fine-grained features by contrasting different soil sample pairs, which takes full advantage of soil particle heterogeneity and NIRS penetration. For the validation of this study, three different soil datasets with various collection sources are selected as research subjects, and the performance of Pair-Soil-Spectra is analyzed from different perspectives. According to the results, Pair-Soil-Spectra has significantly improved the performance of STN content detection models (e.g., partial least-squares (PLS), Cubist, extreme learning machine (ELM), and random forest (RF)) in small sample cases. Of these, the coefficient of determination of RF has improved by 0.13, 0.42, and 0.10, and the root-mean-square of prediction has decreased by 0.15, 0.52, and 0.01 g/kg with different datasets, which has gained the greatest improvement. Meanwhile, this approach can be easily expanded to cover other domains.

The need for large sample numbers to demonstrate that Martian environments are lifeless

The need for large sample numbers to demonstrate that Martian environments are lifeless

Oxidative conversion of transthyretin in formalin-fixed clinical amyloid samples results in the formation of the His90Asp and His90Asn variants

Background Proteomics is routinely used to type clinical amyloid deposits, and offers additional benefit of identifying genetic variants, which can be diagnostically useful. Reviewing the proteomics data for ATTR patients attending our Centre revealed an unusually large number of samples containing a rare pathogenic H90D TTR variant alongside the more common H90N variant. Methods These findings raised questions to their source. Proteomics was used to monitor the generation of H90D/H90N variants in fresh, frozen, stored samples during extraction and digestion, and also following Cu2+-mediated oxidation. Results There was no evidence that the variants were present in the circulation, except in one patient with genetically confirmed H90D TTR, in fresh fat aspirates or tissues from an ATTR amyloid mouse model. The variant could be generated in vitro from both wild-type TTR and ex vivo ATTR fibrils by non-enzymic oxidation of histidine at position 90. These data suggest that the H90D variant can be generated artefactually from wild-type 90H TTR through a radical-mediated oxidation of histidine, followed by its conversion to asparagine and aspartic acid. This probably occurs during storage. Conclusions In the absence of genetic data, the identification of H90D TTR in stored tissue by proteomics should be treated with caution.

FC07 Transcriptomic profiling across tissues and machine learning reveal molecular endotypes of psoriasis phenotypes and signatures of disease severity during biologic therapy

Abstract Despite advances in understanding the pathogenesis of psoriasis, important knowledge gaps exist including (i) whether molecular endotypes of stable plaque psoriasis underlie distinct clinical phenotypes (e.g. subjects with specific comorbidities) and (ii) the positive drivers and negative molecular regulators of disease severity across tissue compartments. We performed a comprehensive RNA-seq study of skin and blood samples (n = 718) from deeply phenotyped discovery (n = 89) and refinement (n = 57) psoriasis cohorts, during targeted treatment with a TNF inhibitor (adalimumab) or an interleukin (IL)-12/23 inhibitor (ustekinumab). As gene expression is controlled by multiple factors across different cell types, network analysis of bulk RNAseq data across large numbers of clinical samples takes into account the underlying latent factors and modules that result in the coordination of gene expression and can more accurately reflect the relationship between transcriptional signatures and clinical phenotypes compared with analysis of individual genes. Using two complementary methods for dimensionality reduction, weighted gene correlation network analysis (WGCNA) and Independent Component Analysis (ICA), we defined distinct but interconnected gene modules and factors within skin and blood that were significantly positively or negatively associated with disease severity as measured by Psoriasis Area Severity Index (PASI). To understand which modules and factors in lesional psoriasis skin contribute to the prediction of disease severity, we applied Gaussian process regression followed by SHAP (SHapley Additive exPlanations). This analysis highlighted 2 principal gene modules [cytokine signalling (267 genes) and Wnt signalling (207 genes)] that explained ∼50% of the variance of PASI during clinical response to both adalimumab and ustekinumab. We derived a further gene signature (14 genes), linked to body mass index in nonlesional skin at baseline, that in lesional skin was significantly negatively associated with PASI. Notably, PASI-associated signatures in blood were only seen following exposure to adalimumab whereas ustekinumab PASI-associated signatures were confined to skin. These highly drug-specific tissue compartmentalization effects indicate a differential systemic impact of adalimumab compared with ustekinumab, in line with differences in side-effect profiles. In contrast, a gene signature in blood closely linked to HLA-C*06:02 status was independent of disease severity (PASI). The systematic nature of the dataset across skin and blood and its integration with key clinical features and outcomes to therapy provides a comprehensive and global perspective of psoriasis that are not apparent from previous studies. Specifically, our findings delineate, across skin and blood, distinct gene-environmental and genetic effects on the psoriasis transcriptome linked to clinical phenotypes and disease severity endotypes.

Wilhelm Friedberg and his scientific legacy at the Natural History Museum in Lviv

The samples of land molluscs collected by M.V. Gensytskyi in 2017-2020 in the North-Western Azov region, mainly in the southern part of the Zaporizhzhia region, as well as in some adjacent areas of the Kherson and Donetsk regions of Ukraine, were transferred in February 2022 to the museum. The malacological collection of the museum was replenished by 637 storage units (samples) and more than 12 thousand specimens (shells of molluscs). The donated materials represent 22 taxa from 11 families. Shells of molluscs of the genera Cochlicopa and Helicopsis require additional identification. Chondrula tridens, represented by the subspecies Ch. tridens martynovi, Vallonia pulchella and V. costata have the largest number of samples. Ch. tridens martynovi, Xeropicta derbentina and Helix albescens predominate in the number of collected shells. Compared to the dissertation of M.V. Gensytskyi, single samples of Pupilla triplicata, Brephulopsis bidens and Xeropicta krynickii were also found in his collection. The sample of B. bidens from the Henichesk district of the Kherson region is the fourth known record of this species in Ukraine outside Crimea. P. triplicata has not been previously mentioned for the Zaporizhzhia region. It turned out that Phenacolimax annularis is more widespread in the south of the Zaporizhzhia region than it was described by M.V. Gensytskyi. In contrary, not a single shell of Monacha cartusiana was found among the materials transferred to the museum. The donated collection significantly supplemented the available stock materials, which represent the land mollusc fauna of the steppe zone of Ukraine in general and Zaporizhzhia region in particular. This will contribute to increasing the scientific potential of the malacological collection of the museum in relation to both the geography of samples and a better representation of various forms of intraspecific conchological variability. From 2022, the territory of research of M.V. Gensytskyi is under Russian occupation. The impact of hostilities may affect biological diversity, the distribution of some species, etc., which further increases the comparative value of such large-scale pre-war collections.

Orthopoxvirus Genome Sequencing, Assembly, and Analysis.

Poxviruses have exceptionally large genomes compared to most other viruses, which represent unique challenges to sequencing and assembly due to complex features such as repeat elements and low complexity sequences. The 2022 global mpox outbreak led to an unprecedented level of poxvirus sequencing as public health and research institutions faced with large sample numbers and demand for fast turnaround, merged NGS protocols designed for small RNA viruses with poxvirus expertise. Traditional manual assembly, checking, and editing of genomes was not feasible. Here, we present a protocol for metagenomic sequencing and orthopoxvirus genome assembly directly from DNA extracted from a patient lesion swab with no viral enrichment or host depletion. This sequencing approach is cost effective when using high throughput sequencing instruments and allows for detection of genomic insertions, deletions, and large rearrangement with confidence. We describe usage of two publicly available bioinformatic pipelines for genome assembly, quality control, annotation, and submission to sequence repositories.

An improved GM(1,1) model based on weighted MSE and optimal weighted background value and its application

GM(1,1) model is widely used because it does not require a large number of samples and has a low computational complexity and no limitation of statistical assumptions. Common drawback of the GM(1,1) models developed by various techniques for improving the performance is their unsatisfied predicting accuracy although they have satisfied fitting accuracy. The aim of this paper is to develop GM(1,1) model with excellent predicting accuracy rather than fitting one. We proposed an improved GM(1,1) model based on weighted mean squared error (MSE) and optimal weighted background value: OB-WMSE-GM(1,1). To illustrate its effectiveness, it was applied to one simulation example and two application examples. For the exponential function simulation example, the fitting MSE, fitting WMSE and predicting MSE of the proposed GM(1,1) (0.000002, 0.000039 and 0.015485) were much lower than ones of the typical GM(1,1) (0.001492, 0.002606 and 1.144524). For the annual LCD TV output prediction example, the fitting MSE, fitting WMSE and predicting MSE of the proposed GM(1,1) (1.425255, 3.199446 and 132.046775) were much lower than ones of the typical GM (1,1) (48.003290, 111.431942 and 5519.135753). For the crude oil processing volume prediction example, the fitting MAPE, fitting WMRE and predicting MAPE of the proposed GM(1,1) (4.090405, 3.213106 and 3.775669) were lower than ones of the typical GM(1,1) (4.399448, 3.883765 and 5.040509). When the proposed method is properly combined with the other various techniques including metabolic mechanism, residual GM(1,1), original sequence pre-processing, background value reconstruction and initial condition optimization, its performance may be more and more improved.