Integrative metagenomic, transcriptomic, and proteomic analysis reveal the microbiota-host interplay in early-stage lung adenocarcinoma among non-smokers.

BACKGROUND: The incidence of early-stage lung adenocarcinoma (ES-LUAD) is steadily increasing among non-smokers. Previous research has identified dysbiosis in the gut microbiota of patients with lung cancer. However, the local microbial profile of non-smokers with ES-LUAD remains largely unknown. In this study, we systematically characterized the local microbial community and its associated features to enable early intervention. METHODS: A prospective collection of ES-LUAD samples (46 cases) and their corresponding normal tissues adjacent to the tumor (41 cases), along with normal lung tissue samples adjacent to pulmonary bullae in patients with spontaneous pneumothorax (42 cases), were subjected to ultra-deep metagenomic sequencing, host transcriptomic sequencing, and proteomic sequencing. The obtained omics data were subjected to both individual and integrated analysis using Spearman correlation coefficients. RESULTS: We concurrently detected the presence of bacteria, fungi, and viruses in the lung tissues. The microbial profile of ES-LUAD exhibited similarities to NAT but demonstrated significant differences from the healthy controls (HCs), characterized by an overall reduction in species diversity. Patients with ES-LUAD exhibited local microbial dysbiosis, suggesting the potential pathogenicity of certain microbial species. Through multi-omics correlations, intricate local crosstalk between the host and local microbial communities was observed. Additionally, we identified a significant positive correlation (rho > 0.6) between Methyloversatilis discipulorum and GOLM1 at both the transcriptional and protein levels using multi-omics data. This correlated axis may be associated with prognosis. Finally, a diagnostic model composed of six bacterial markers successfully achieved precise differentiation between patients with ES-LUAD and HCs. CONCLUSIONS: Our study depicts the microbial spectrum in patients with ES-LUAD and provides evidence of alterations in lung microbiota and their interplay with the host, enhancing comprehension of the pathogenic mechanisms that underlie ES-LUAD. The specific model incorporating lung microbiota can serve as a potential diagnostic tool for distinguishing between ES-LUAD and HCs.

Buccal Swab Samples from Japanese Brown Cattle Fed with Limonite Reveal Altered Rumen Microbiome.

The areas of the Mount Aso grasslands in Kumamoto, Japan, are the primary location for the breeding of the Kumamoto strain of Japanese Brown cattle (JBRK). Although Aso limonite, deposited by volcanic ash and magma, has been commonly fed to pregnant JBRK in this area, the mechanisms of its salutary effects on pregnant JBRK have not yet been elucidated. Approximately 100 days before the expected day of calf delivery, seven JBRK (four supplemented with limonite and three controls without limonite) were assigned to this study, from which a buccal swab was collected at the highest rumination every 30 days for 90 days. DNA extracted from these swabs was then analyzed using a 16S rRNA gene amplicon sequence analysis. Statistically significant differences between the two groups were discovered through beta-diversity analysis, though results from alpha-diversity analysis were inconclusive. The microbiota identified were classified into six clusters, and three of the main clusters were core-rumen bacteria, primarily cellulose digestion in cluster 1, oral bacteria in cluster 2, and non-core-rumen bacteria in cluster 3. In the limonite group, core-rumen bacteria decreased while non-core-rumen bacteria increased, suggesting that limonite feeding alters rumen microbiota, particularly activation of non-core-rumen microbiota.

Investigation of ion association kinetics in Na2SO4-H2O by excess Raman spectroscopy and 2D correlation Raman spectroscopy.

The ion association of salts aqueous solutions have long captivated the attention of researchers within the field of physical chemistry. In this paper, we have performed a comprehensive analysis of ion interactions in sodium sulfate (Na2SO4) aqueous solutions using a combination of high-resolution techniques, including excess (ERS) and two-dimensional correlation (2DCRS) Raman spectroscopy in conjunction with molecular dynamics (MD) calculations. The Raman spectrum shows that two inflection points in the Raman shift of the O-H vibration are observed with the increase in Na2SO4 concentration. Simultaneously, a new peak of the SO42- vibration appears at first inflection point, representing the formation of ion association. Further analysis based on ERS and 2CRS reveals that these two inflection points correspond respectively to the formation of ion pairs (CIPs) and small ion clusters. Importantly, MD simulations confirm the above experimental results. Our study provides evidence for ion association and clustering in aqueous in salt ion aqueous solutions.

Selective correlations between aliphatic 13C nuclei in protein solid-state NMR.

Solid-state nuclear magnetic resonance (NMR) is a potent tool for studying the structures and dynamics of insoluble proteins. It starts with signal assignment through multi-dimensional correlation experiments, where the aliphatic 13Cα-13Cß correlation is indispensable for identifying specific residues. However, developing efficient methods for achieving this correlation is a challenge in solid-state NMR. We present a simple band-selective zero-quantum (ZQ) recoupling method, named POST-C4161 (PC4), which enhances 13Cα-13Cß correlations under moderate magic-angle spinning (MAS) conditions. PC4 requires minimal 13C radio-frequency (RF) field and proton decoupling, exhibits high stability against RF variations, and achieves superior efficiency. Comparative tests on various samples, including the formyl-Met-Leu-Phe (fMLF) tripeptide, microcrystalline ß1 immunoglobulin binding domain of protein G (GB1), and membrane protein of mechanosensitive channel of large conductance from Methanosarcina acetivorans (MaMscL), demonstrate that PC4 selectively enhances 13Cα-13Cß correlations by up to 50 % while suppressing unwanted correlations, as compared to the popular dipolar-assisted rotational resonance (DARR). It has addressed the long-standing need for selective 13C-13C correlation methods. We anticipate that this simple but efficient PC4 method will have immediate applications in structural biology by solid-state NMR.

Relationships among soil moisture at various depths under diverse climate, land cover and soil texture.

Soil moisture is an important component of the hydrological cycle and a key mediator between land surface and atmospheric interactions. Although substantial progress has been made in remote sensing of soil moisture at different spatial scales, the shallow penetration depth of remote sensors greatly limits their utility for applications in meteorological modelling and hydrological studies where the critical variable of interest is the root-zone soil moisture content. Therefore, this study assesses the relationship between soil moisture at the surface (10 cm) and in lower soil layers (20, 40, 60, 80, 100, and 120 cm) under varying climates, soils, and vegetation types. Cross-correlation analysis is applied to daily in-situ soil moisture measurements from 4712 locations in agricultural lands across the contiguous United States. Our analysis demonstrates that zero-day lag always produced the highest correlation between 10 cm soil moisture and soil moisture in the lower layers. In addition, a positive and strong relationship between 10 and 20 cm soil moisture (r = 0.84) was observed, while the relationships between 10 and 40 cm soil moisture were moderate (r = 0.52). The decline in cross-correlation continued to the deeper soil layers, which indicated that, on a daily timescale, the surface soil moisture gradually becomes decoupled with soil moisture at greater depths. Therefore, our research suggests that the estimation of soil moisture in the soil layers <40 cm based on surface soil moisture is most promising. However, the influence of climate, land cover, and soil textures on the strength of relationships between surface and lower layers makes the prediction difficult. The comparatively weak relationship between precipitation and soil moisture (0.09-0.32), as well as the relationship between reference evapotranspiration (ETo) and soil moisture (-0.19-0.18), in this study can be attributed to scale mismatching from different data sources.

Tunable charge transport properties in non-stoichiometric SrIrO3 thin films.

Delving into the intricate interplay between spin-orbit coupling and Coulomb correlations in strongly correlated oxides, particularly perovskite compounds, has unveiled a rich landscape of exotic phenomena ranging from unconventional superconductivity to the emergence of topological phases. In this study, we have employed pulsed laser deposition (PLD) technique to grow SrIrO3 (SIO) thin films on SrTiO3 substrates, systematically varying the oxygen content during the post-deposition annealing. X-ray Photoelectron Spectroscopy (XPS) provided insights into the stoichiometry and spin-orbit splitting energy of Iridium within the SIO film, while high-resolution X-ray studies meticulously examined the structural integrity of the thin films. Remarkably, our findings indicate a decrease in the metallicity of SIO thin films with reduced annealing O2 partial pressure. Furthermore, we carried out magneto-transport studies on the SIO thin films, the results revealed intriguing insights into spin transport as a function of oxygen content. The tunability of the electronic band structure of SrIrO3 films with varying oxygen vacancy is correlated with the DFT calculations. Our findings elucidate the intricate mechanisms dictating spin transport properties in SrIrO3 thin films, offering invaluable guidance for the design and optimization of spintronic devices based on complex oxide materials. Notably, the ability to tune bandwidth by varying post-annealing oxygen partial pressure in iridate-based spintronic materials holds significant promise for advancing technological applications in the spintronics domain. .

Discovering 3D hidden elasticity in isotropic and transversely isotropic materials with physics-informed UNets.

Three-dimensional variation in structural components or fiber alignments results in complex mechanical property distribution in tissues and biomaterials. In this paper, we use a physics-informed UNet-based neural network model (El-UNet) to discover the three-dimensional (3D) internal composition and space-dependent material properties of heterogeneous isotropic and transversely isotropic materials without a priori knowledge of the composition. We then show the capabilities of El-UNet by validating against data obtained from finite-element simulations of two soft tissues, namely, brain tissue and articular cartilage, under various loading conditions. We first simulated compressive loading of 3D brain tissue comprising of distinct white matter and gray matter mechanical properties undergoing small strains with isotropic linear elastic behavior, where El-UNet reached mean absolute relative errors under 1.5 % for elastic modulus and Poisson's ratio estimations across the 3D volume. We showed that the 3D solution achieved by El-UNet was superior to relative stiffness mapping by inverse of axial strain and two-dimensional plane stress/plane strain approximations. Additionally, we simulated a transversely isotropic articular cartilage with known fiber orientations undergoing compressive loading, and accurately estimated the spatial distribution of all five material parameters, with mean absolute relative errors under 5 %. Our work demonstrates the application of the computationally efficient physics-informed El-UNet in 3D elasticity imaging and provides methods for translation to experimental 3D characterization of soft tissues and other materials. The proposed El-UNet offers a powerful tool for both in vitro and ex vivo tissue analysis, with potential extensions to in vivo diagnostics. STATEMENT OF SIGNIFICANCE: Elasticity imaging is a technique that reconstructs mechanical properties of tissue using deformation and force measurements. Given the complexity of this reconstruction, most existing methods have mostly focused on 2D problems. Our work is the first implementation of physics-informed UNets to reconstruct three-dimensional material parameter distributions for isotropic and transversely isotropic linear elastic materials by having deformation and force measurements. We comprehensively validate our model using synthetic data generated using finite element models of biological tissues with high bio-fidelity-the brain and articular cartilage. Our method can be implemented in elasticity imaging scenarios for in vitro and ex vivo mechanical characterization of biomaterials and biological tissues, with potential extensions to in vivo diagnostics.

Can next generation ecological risk assessment decisions be made today?-A case study of regulatory risk assessment in the United States.

We examined the need for new in vivo avian toxicity testing for three common industrial chemicals (1,2 dichloropropane, 1,1,2-trichloroethane and triphenyl phosphate) based on estimated avian exposures using fugacity and multimedia fate models for current conditions of use compared to hazard information including existing in vivo test data for the chemicals and analogs, interspecies correlation estimates and results from hundreds of acute avian dietary toxicity studies. The data indicated that acute avian toxicity is not likely to be observed below 10 ppm in the diet for any chemical with the exception of those with a specific mode of toxic action. Modeling indicated low exposure potential for terrestrial birds to any of the three chemicals, with estimated dietary concentration of less than 0.001 ppm. Despite uncertainty associated with the underlying data sources, the four order of magnitude gap between potential exposure and a minimum hazard threshold suggests that additional avian in vivo testing would not generate valuable data. However, a weight of evidence approach for integrating data is necessary to engender greater confidence among government decision-makers in cases where data from a particular in vivo study is not expected to improve risk decision-making and an existing data gap can remain unfilled.

The Prognostic and Therapeutic Potential of Fragile X Mental Retardation 1 (FMR1) Gene Expression in Prostate Adenocarcinoma: Insights into Survival Outcomes and Oncogenic Pathway Modulation.

Prostate adenocarcinoma (PRAD) is the second most common tumor associated with death. The role and mechanisms of the fragile X mental retardation 1 (FMR1) gene in PRAD remain unknown. We conducted an analysis of FMR1 expression in PRAD to determine its prognostic importance and connection to carcinogenic pathways such as PI3K_AKT_mTOR. Survival analyses were utilized to establish a correlation between FMR1 expression and patient outcomes. We used the integration of genomic data with bioinformatic predictions to predict the regulatory factors of the FMR1 gene in PRAD. Our data revealed that individuals with higher levels of FMR1 expression experience worse survival outcomes compared to those with lower expression (hazard ratio [HR] = 5.08, 95% confidence interval [CI] = 1.07 - 24, p = 0.0412). FMR1 expression was significantly higher in patients with advanced pathological tumor stages, particularly in the pT3 and pT4 combined stages and the pN1 nodal stage. Furthermore, patients with high Gleason scores (GSs) (combined GSs 8 and 9) exhibited increased levels of FMR1 expression. Our results further identify a possible regulatory link between FMR1 and key oncogenic pathways, including PI3K_AKT_mTOR, and predict the possible mechanism by which FMR1 is regulated in PRAD. Our data suggest that the FMR1 gene could serve as a biomarker for PRAD progression. However, in-depth investigations, including those with large patient samples and in vitro studies, are needed to validate this finding and understand the mechanisms involved.

A simple projection method to correlate the principal mechanical direction with the principal microstructural direction of human osteoporotic femoral heads.

The mechanics of the trabecular bone is related to its structure; this work aimed to propose a simple projection method to clarify the correlation between the principal mechanical direction (PMD) and the principal microstructural direction (PMSD) of trabecular bones from osteoporotic femoral heads. A total of 529 trabecular cubes were cropped from five osteoporotic femoral heads. The micro computed tomography (µCT) sequential images of each cube were first projected onto the three Cartesian coordinate planes to have three overlapped images, and the trabecular orientation distribution in the three images was analyzed. The PMSD corresponding to the greatest distribution frequency of the trabecular orientation in the three images was defined. Then, the voxel finite element (FE) models of the cubes were reconstructed and simulated to obtain their compliance matrices, and the matrices were subjected to transversal rotation to find their maximum elastic constants. The PMD corresponding to the maximum elastic constant was defined. Subsequently, the correlation of the defined PMSD and PMD was analyzed. The results showed that PMSD and PMD of the trabecular cubes did not show a significant difference at the xy- and yz-planes except that at the zx-plane. Despite this, the mean PMSD-PMD deviations at the three coordinate planes were close to 0°, and the PMSD-PMD fitting to the line PMSD = PMD demonstrated their high correlation. This study might be helpful to identify the loading direction of anisotropic trabecular bones in experiments by examining the PMSD and also to guide bone scaffold design for bone tissue repair.

Genotype-Phenotype Correlation in Neurofibromatosis Type 1: Evidence for a Mild Phenotype Associated with Splicing Variants Leading to In-Frame Skipping of NF1 Exon 24 [19a].

Neurofibromatosis type 1 (NF1) is an autosomal dominant neurocutaneous disorder caused by loss-of-function variants in the NF1 gene. As of 20 November 2023, over 5000 distinct pathogenic or likely pathogenic variants have been reported in public databases. However, only a few NF1 genotype-phenotype correlations have been established so far. In this study, we present findings on 40 individuals with NF1, comprising 26 unrelated probands and 14 affected relatives, who carry one of nine NF1 heterozygous pathogenic splicing variants, all of which result in the in-frame skipping of exon 24 [19a] (NM_000267.3:r.3114_3197del, p.Asn1039_Arg1066del). These variants include c.3114-2A>G, c.3114-1G>A, c.3196A>G, c.3197G>A, c.3197G>T, c.3197+1G>A, c.3197+1G>T, c.3197+2T>C, and c.3197+3A>T. Among individuals with these variants, none exhibit externally visible plexiform neurofibromas, histopathologically confirmed cutaneous or subcutaneous neurofibromas, symptomatic spinal neurofibromas, or symptomatic optic pathway gliomas. The most prevalent, and sometimes sole, clinical feature observed in this cohort is multiple café-au-lait macules, with or without skinfold freckles: 85% and 60.5% of the individuals display six or more café-au-lait macules and freckles, respectively. In comparison to established NF1 genotype-phenotype correlations, these patients demonstrate highly similar clinical presentations to those associated with the NF1 pathogenic variant c.2970_2972del (p.Met992del), known for resulting in the mildest clinical features. Despite the generally mild phenotype, cognitive impairment, developmental delay, and/or learning difficulties are still observed in 33.3% of these patients, suggesting that learning challenges remain a prominent aspect of the phenotypic presentation in these individuals and necessitate specialized care. This newly established genotype-phenotype correlation will assist clinicians in improving the management of patients harboring NF1 exon 24 [19a] skipping variants and provide a new therapeutic target for NF1 treatment.

Dynamic water migration and flavor analysis of sea cucumber in the process of Sichuan pepper seasoning soak.

Soaking in seasoning solution is the main process of sea cucumber seasoning. This study analyzed the dynamic changes in water migration and flavor substances in sea cucumbers during soaking in a Sichuan pepper solution. It was found that the sea cucumber experienced a process of water absorption followed by water loss during the 0-48 h soaking process. During this period, the flavor compounds in sea cucumbers showed different dynamic trends. A total of 46 volatiles were identified, of which 29 were key flavor compounds. Its flavor profiles tended to stabilize as soaking time increased. m-Xylene, d-Limonene, Eucalyptol, p-Xylene, Sabinene, Beta-Myrcene, and Beta-Phellandrene were the main characteristic substances contributing to the differences in sea cucumber flavor. Correlation analysis predicted the relationship between water migration and the dynamic shifts in flavor compounds. This study provides a crucial reference for future studies on the processing and flavor modulation of sea cucumber products.

Influence of additive and maternal effects on production and reproduction traits in Murrah buffaloes: Insights from Bayesian analysis.

The aim of this research was to assess genetic parameters for first lactation production and reproduction traits in Murrah buffaloes by employing additive and maternal effects. Data on pedigree and specific traits of 640 Murrah buffaloes were gathered from 1997 to 2020. These traits encompassed first lactation milk yield (FLMY), 305-day first lactation milk yield (305FLMY), first lactation length (FLL), first lactation peak yield (FPY), first service period (FSP), first calving interval (FCI) and first dry period (FDP). Genetic evaluations employed six univariate animal models, accounting for both direct and maternal effects, facilitated by THRGIBBS1F90 and POSTGIBBSF90 programs. Fixed factors included in the analysis were period of calving, season of calving and age at first calving. The Bayesian estimates for direct heritability, derived from the most suitable model, were as follows: FLMY: 0.28 ± 0.01, 305FLMY: 0.30 ± 0.01, FLL: 0.19 ± 0.01, FPY: 0.18 ± 0.01, FSP: 0.12 ± 0.01, FCI: 0.14 ± 0.01 and FDP: 0.12 ± 0.01. Maternal effects were found significant, ranging from 5% to 10%, in first lactation traits under Model 2 and Model 5. Additionally, positive and significant genetic and phenotypic correlations were observed among the studied traits. In conclusion, selection based on 305-day first lactation milk yield suggests potential for genetic enhancement in Murrah buffaloes, advocating its inclusion in breeding programmes to bolster early performance. Also, consideration of maternal influences is necessary for genetic progress of animals.

Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation.

Heritability and genetic covariance/correlation quantify the marginal and shared genetic effects across traits. They offer insights on the genetic architecture of complex traits and diseases. To explore how genetic variations contribute to brain function variations, we estimated heritability and genetic correlation across cortical thickness, surface area, and volume of 33 anatomically predefined regions in left and right hemispheres, using summary statistics of genome-wide association analyses of 31,968 participants in the UK Biobank. To characterize the relationships between these regions of interest, we constructed a genetic network for these regions using recursive two-way cut-offs in similarity matrices defined by genetic correlations. The inferred genetic network matches the brain lobe mapping more closely than the network inferred from phenotypic similarities. We further studied the associations between the genetic network for brain regions and 30 complex traits through a novel composite-linkage disequilibrium score regression method. We identified seven significant pairs, which offer insights on the genetic basis for regions of interest mediated by cortical measures.

Prevalence and factors associated with fatigue in patients with major depressive disorder or bipolar disorder.

AIMS: To study the prevalence of fatigue and factors associated with fatigue in patients with major depressive disorder (MDD) or bipolar disorder (BD). METHODS: Two hundred fifty-three outpatients with MDD or BD at the initial assessment were used to study the prevalence of fatigue and relationship between fatigue and other clinical correlates. The severity of fatigue was measured with Iowa Fatigue Scale (IFS), and depression and anxiety symptom-severity were measured with the QIDS-16-SR (the 16-item Quick Inventory of Depressive Symptomatology - Self-Report) and Zung-SAS (Zung Self-Rating Anxiety Scale). Correlation between IFS and QIDS-16-SR total scores, QIDS-16-SR item scores or Zung-SAS total scores, and independent factors associated with fatigue was assessed with simple or multiple linear regression analysis. RESULTS: Overall, 28.4 % of MDD and 29.8 % of BD patients did not have fatigue, but 41.2 % of MDD and 45.0 % of BD patients had fatigue, and 30.4 % of MDD and 25.2 % of BD patients had severe fatigue. Depression/anxiety severity was significantly correlated with fatigue. However, after controlling current psychiatric comorbidities, demographics, some social factors, and psychotropic use, only QIDS-16-SR scores were still significantly and positively correlated with IFS scores in both MDD and BD. Differential correlations between IFS scores and item scores of QIDS-16-SR in MDD and BD were observed. LIMITATION: Cross-sectional. CONCLUSIONS: In this outpatient sample, fatigue was highly prevalent in patients with MDD or BD. The independent association of depressive severity with the severity of fatigue highlights the importance of complete resolution of depressive symptoms in treating MDD and BD.

A novel temporal-frequency combination pattern optimization approach based on information fusion for motor imagery BCIs.

Motor imagery (MI) stands as a powerful paradigm within Brain-Computer Interface (BCI) research due to its ability to induce changes in brain rhythms detectable through common spatial patterns (CSP). However, the raw feature sets captured often contain redundant and invalid information, potentially hindering CSP performance. Methodology-wise, we propose the Information Fusion for Optimizing Temporal-Frequency Combination Pattern (IFTFCP) algorithm to enhance raw feature optimization. Initially, preprocessed data undergoes simultaneous processing in both time and frequency domains via sliding overlapping time windows and filter banks. Subsequently, we introduce the Pearson-Fisher combinational method along with Discriminant Correlation Analysis (DCA) for joint feature selection and fusion. These steps aim to refine raw electroencephalogram (EEG) features. For precise classification of binary MI problems, an Radial Basis Function (RBF)-kernel Support Vector Machine classifier is trained. To validate the efficacy of IFTFCP and evaluate it against other techniques, we conducted experimental investigations using two EEG datasets. Results indicate a notably superior classification performance, boasting an average accuracy of 78.14% and 85.98% on dataset 1 and dataset 2, which is better than other methods outlined in this article. The study's findings suggest potential benefits for the advancement of MI-based BCI strategies, particularly in the domain of feature fusion.

The role of the radiologist in the prostate cancer multidisciplinary conference.

The broad range of disease aggressiveness together with imperfect screening, diagnostic, and treatment options in prostate cancer (PCa) makes medical decision-making complex. The primary goal of a multidisciplinary conference is to improve patient outcomes by combining evidence-based data and expert opinion to discuss optimal management, including for those patients with challenging presentations. The primary purpose of the genitourinary imaging specialist in the prostate cancer multidisciplinary conference is to use imaging findings to reduce uncertainty by answering clinical questions. In this review, we discuss the role and the opportunities for an imaging specialist to add value in the care of men with prostate cancer discussed at multidisciplinary conferences.

[Construction and application of a risk index of Echinococcus infection based on the classification of echinococcosis lesions].

OBJECTIVE: To investigate the feasibility of constructing the risk index of Echinococcus infection based on the classification of echinococcosis lesions, so as to provide insights into the management of echinococcosis. METHODS: The imaging data of echinococcosis cases were collected from epidemiological surveys of echinococcosis in China from 2012 to 2016, and the detection of incident echinococcosis cases was captured from the annual echinococcosis prevention and control reports across provinces (autonomous regions) and Xinjiang Production and Construction Corps in China from 2017 to 2022. After echinococcosis lesions were classified, a risk index of Echinococcus infection was constructed based on the principle of discrete distribution marginal probability and multi-group classification data tests. The correlation between the risk index of Echinococcus infection and the detection of incident echinococcosis cases was evaluated in the provinces (autonomous regions and corps) from 2017 to 2022, and the correlations between the short and medium-term risk indices and between the medium and long-term risk indices of Echinococcus infection were examined using a univariate linear regression model. RESULTS: A total of 4 014 echinococcosis cases in China from 2012 to 2016 were included in this study. The short-, medium- and long-term risk indices of E. granulosus infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ2 = 4.12 to 708.65, all P values < 0.05), with high short- (0.058), medium- (0.137) and long-term risk indices (0.104) in Tibet Autonomous Region, and the short-, medium- and long-term risk indices of E. multilocularis infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ2 = 6.74 to 122.60, all P values < 0.05), with a high short-term risk index in Sichuan Province (0.016) and high medium- (0.009) and long-term risk indices in Qinghai Province (0.018). There were no significant correlations between the risk index of E. granulosus infection and the detection of incident cystic echinococcosis cases during the study period (t = -0.518 to 2.265, all P values > 0.05), and strong correlations were found between the risk indices of E. multilocularis infection and the detection of incident alveolar echinococcosis cases (including mixed type) in 2018, 2020, 2021, 2022, during the period from 2017 through 2020, from 2017 through 2021, from 2017 through 2022 (all r values > 0.7, t = 2.521 to 3.692, all P values < 0.05). Linear regression models were established between the risk index of E. multilocular infection and the detection of alveolar echinococcosis cases (including mixed type), and the models were all statistically significant (b = 0.214 to 2.168, t = 2.458 to 3.692, F = 6.044 to 13.629, all P values < 0.05). The regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of E. granulosus infection were 2.339 and 0.765, and the regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of E. multilocular infection were 0.280 and 1.842, with statistical significance seen in both the regression coefficients and regression models (t = 16.479 to 197.304, F = 271.570 to 38 928.860, all P values < 0.05). CONCLUSIONS: The risk index of Echinococcus infection has been successfully established based on the classification of echinococcosis lesions, which may provide insights into the prevention and control, prediction, diagnosis and treatment, and classified management of echinococcosis.

Cyclostationary analysis of uterine EMG measurements for the prediction of preterm birth.

Preterm birth (gestational age < 37 weeks) is a public health concern that causes fetal and maternal mortality and morbidity. When this condition is detected early, suitable treatment can be prescribed to delay labour. Uterine electromyography (uEMG) has gained a lot of attention for detecting preterm births in advance. However, analyzing uEMG is challenging due to the complexities associated with inter and intra-subject variations. This work aims to investigate the applicability of cyclostationary characteristics in uEMG signals for predicting premature delivery. The signals under term and preterm situations are considered from two online datasets. Preprocessing is carried out using a Butterworth bandpass filter, and spectral correlation density function is adapted using fast Fourier transform-based accumulation method (FAM) to compute the cyclostationary variations. The cyclic frequency spectral density (CFSD) and degree of cyclostationarity (DCS) are quantified to assess the existence of cyclostationarity. Features namely, maximum cyclic frequency, bandwidth, mean cyclic frequency (MNCF), and median cyclic frequency (MDCF) are extracted from the cyclostationary spectrum and analyzed statistically. uEMG signals exhibit cyclostationarity property, and these variations are found to distinguish preterm from term conditions. All the four extracted features are noted to decrease from term to preterm conditions. The results indicate that the cyclostationary nature of the signals can provide better characterization of uterine muscle contractions and could be helpful in detecting preterm birth. The proposed method appears to aid in detecting preterm birth, as analysis of uterine contractions under preterm conditions is imperative for timely medical intervention.

Femorotibial angle on short knee radiographs fails to accurately predict the lower limb mechanical alignment. A systematic review and meta-analysis on different femorotibial angle definitions and short knee radiograph types.

Introduction: Hip-knee-ankle angle (HKA) on Full Limb Radiographs (FLRs) is the gold standard for coronal knee alignment assessment. Despite the widespread utilization of the more convenient femorotibial angle (FTA) on either antero-posterior (AP) or postero-anterior (PA) short knee radiographs (SKRs), its definition and correlation with HKA remains controversial. This review is the first to systematically investigate FTA-HKA correlation and the effect of different FTA methods and SKRs. Methods: Systematic literature search (Pubmed, Scopus, Cochrane Library) followed PRISMA guidelines, to evaluate studies examining the FTA-HKA correlation. Meta-analyses compared the 3 most common FTA methods, knee center determination method and SKR types. Results: 17 studies (2597 patients, 3234 knees) were included. The strongest correlation with HKA (r = 0.78) was found for FTA Method 1 (angle formed by lines drawn from the midpoint of tibial spines to points 10 cm above and below the joint line). No significant differences were observed when grouping the FTA methods by knee center assessment (Group I, r = 0.78; Group II, r = 0.77). AP SKRs showed a trend towards stronger FTA-HKA correlation compared to PA SKRs, in both Method 1 (r = 0.79 vs 0.75) and Method 3 (r = 0.80 vs 0.66). Conclusion: Irrespective of its definition or type of SKR used, FTA lacks reliable accuracy in predicting the HKA in most knees. FLRs should be used whenever precise estimation of the patient's alignment is necessary. Caution is warranted in interpreting studies investigating knee alignment or knee arthroplasty outcomes based on FTA.