Statistical modeling and significance estimation of multi-way chromatin contacts with HyperloopFinder.

Recent advances in chromatin conformation capture technologies, such as SPRITE and Pore-C, have enabled the detection of simultaneous contacts among multiple chromatin loci. This has made it possible to investigate the cooperative transcriptional regulation involving multiple genes and regulatory elements at the resolution of a single molecule. However, these technologies are unavoidably subject to the random polymer looping effect and technical biases, making it challenging to distinguish genuine regulatory relationships directly from random polymer interactions. Here, we present HyperloopFinder, a method for identifying regulatory multi-way chromatin contacts (hyperloops) by jointly modeling the random polymer looping effect and technical biases to estimate the statistical significance of multi-way contacts. The results show that our model can accurately estimate the expected interaction frequency of multi-way contacts based on the distance distribution of pairwise contacts, revealing that most multi-way contacts can be formed by randomly linking the pairwise contacts adjacent to each other. Moreover, we observed the spatial colocalization of the interaction sites of hyperloops from image-based data. Our results also revealed that hyperloops can function as scaffolds for the cooperation among multiple genes and regulatory elements. In summary, our work contributes novel insights into higher-order chromatin structures and functions and has the potential to enhance our understanding of transcriptional regulation and other cellular processes.

Drug exposure and treatment outcomes in patients with multidrug resistant tuberculosis and diabetes mellitus: A multicenter prospective cohort study from China.

BACKGROUND: The management of multidrug-resistant tuberculosis (MDR-TB) remains challenging. Treatment outcome is influenced by multiple factors, the specific roles of diabetes and glycemic control remain uncertain. This study aims to assess the impact of glycemic control on drug exposure, to investigate the association between drug exposure and treatment outcomes, and to identify clinically-significant thresholds predictive of treatment outcome, among patients with diabetes. METHODS: This multicenter prospective cohort study involved patients with confirmed MDR-TB and diabetes. Drug exposure level was estimated by noncompartmental analysis. The minimum inhibitory concentrations were determined for the individual Mycobacterium tuberculosis isolates. The influence of poor glycemic control (hemoglobin A1c ≥ 7%) on drug exposure and the associations between drug exposure and treatment outcome were evaluated by univariate and multivariate analysis. Classification and regression tree analysis was used to identify the drug exposure/susceptibility thresholds. RESULTS: Among the 131 diabetic participants, 43 (32.8%) exhibited poor glycemic control. Poor glycemic control was independently associated with decreased exposure to moxifloxacin, linezolid, bedaquiline, and cycloserine, but not clofazimine. Additionally, a higher ratio of drug exposure to susceptibility was found to be associated with a favorable MDR-TB treatment outcome. Thresholds predictive of 6-month culture conversion and favorable outcome were bedaquiline AUC/MIC ≥ 245 and moxifloxacin AUC/MIC ≥ 67, demonstrating predictive accuracy in patients, regardless of their glycemic control status. CONCLUSIONS: Glycemic control and optimal TB drug exposure are associated with improved treatment outcomes. This dual management strategy should be further validated in randomized controlled trials of patients with MDR-TB and diabetes.

Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS.

BACKGROUND: The multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified. METHODS: We conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers of ARDS prognosis. RESULTS: In the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the protein-protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P = 0.008). INTERPRETATION: Our multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, offering valuable insights into the prognosis of individuals with ARDS.

The first-principles study of interfacial bonding strength and segregation behavior of alloyed elements at the η(MgZn2)/Al interface.

For precipitation-strengthened Al alloys, the interfacial segregation behavior of alloying elements plays an important role in controlling the effectiveness of precipitation strengthening. In this work, the adhesion work (Wad) and interfacial energy (γ) of the η(0001)/Al(111) interface were studied to gain an insight into the interface properties between the precipitate η and the Al matrix. Additionally, we examined the impact of the segregation behavior of alloyed elements on the bonding strength of the interface. The computed values for Wad and interfacial energies indicated that the T6S3 terminated configuration represents the interfacial structure with the highest stability across all models analyzed. Focusing on the T6S3 interface, the assessed segregated energies (Eseg) disclose that the segregation ability of elements from strong to weak exhibits the order of Ti > Sc > Zr > Y > Ta > Nb > Lu > Hf > Mo > V > W, while Cr and Mn elements are not easy to segregate at the interface. Sc, Ti, V, Cr, Mn, Zr, Nb, Mo, Hf, and Ta preferentially occupy Al atoms, whereas Y and Lu predominantly inhabit Mg atoms. Relative to the clean interface, the electron cloud enrichment at the interface after alloying element X (Zr, Sc, Ti, W, Hf, Mn, Y, Lu and V) doping is weakened, and the ion interaction among interface atoms is enhanced. After doping alloying element X (Nb, Mo, Ta, and Cr), the degree of electron cloud enrichment at the interface is obviously enhanced, and the covalent interaction among interface atoms is enhanced. This suggests that the introduction of alloyed elements through doping can augment the bond strength at the interface between the precipitated phase and matrix, thereby reinforcing the strength and toughness of 7xxx series alloys.

Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations.

The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO2 column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO2 observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO2 is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available.

Emergence and evolution of drug-resistant Mycobacterium tuberculosis in eastern China: A six-year prospective study.

Understanding the emergence and evolution of drug resistance can inform public health intervention to combat tuberculosis (TB). In this prospective molecular epidemiological surveillance study from 2015 to 2021 in eastern China, we prospectively collected whole-genome sequencing and epidemiological data on TB patients. We dissect the ordering of drug resistance mutation acquisition for nine commonly used anti-TB drugs, and we found that the katG S315T mutation first appeared around 1959, followed by rpoB S450L (1969), rpsL L43A (1972), embB M306V (1978), rrs 1401 (1981), fabG1 (1982), pncA (1985) and folC (1988) mutations. GyrA gene mutations appeared after the year of 2000. We observed that the first expansion of Mycobacterium tuberculosis (M.tb) resistance population among eastern China appeared after the introduction of isoniazid, streptomycin and para-amino salicylic acid, and the second expansion after the ethambutol, rifampicin, pyrazinamide, ethionamide and aminoglycosides. We speculate these two expansions are linked with population shift historically. By geospatial analysis, we found drug-resistant isolates migrated within eastern China. With epidemiological data of clonal strains, we observed some strains can evolve continuously in individuals and transmit readily in a population. In conclusion, this study mirrored the emergence and evolution of drug-resistant M.tb in eastern China were linked to the sequence and timing of introduction of anti-TB drugs, and multiple factors may contribute to the resistant population enlarged. To resolve the epidemic of drug-resistant TB, it requires applying anti-TB drugs carefully and/or identifying resistant patients timely to prevent them from developing high-level resistance and transmitting to others.

Evolution and epidemic success of Mycobacterium tuberculosis in eastern China: evidence from a prospective study.

BACKGROUND: Lineage distribution of Mycobacterium tuberculosis (Mtb) isolates is strongly associated with geographically distinct human populations, and its transmission can be further impacted by the bacterial genome. However, the epidemic success of Mtb isolates at an individual level was unknown in eastern China. Knowledge regarding the emergence and transmission of Mtb isolates as well as relevant factors may offer a new solution to curb the spread of the disease. Thus, this study aims to reveal the evolution and epidemic success of Mtb isolates in eastern China. RESULTS: Of initial 1040 isolates, 997 were retained after removing duplicates and those with insufficient sequencing depth. Of the final samples, 733 (73.52%) were from Zhejiang Province, and 264 (26.48%) were from Shanghai City. Lineage 2 and lineage 4 accounted for 80.44% and 19.56%, with common ancestors dating around 7017 years ago and 6882 years ago, respectively. Sub-lineage L2.2 (80.34%) contributed the majority of total isolates, followed by L4.4 (8.93%) and L4.5 (8.43%). Additionally, 51 (5.12%) isolates were identified to be multidrug-resistant (MDR), of which 21 (29.17%) were pre-extensively drug-resistant (pre-XDR). One clade harboring katG S315T mutation may date back to 65 years ago and subsequently acquired mutations conferring resistance to another five antibiotic drugs. The prevalence of compensatory mutation was the highest in pre-XDR isolates (76.19%), followed by MDR isolates (47.06%) and other drug-resistant isolates (20.60%). Time-scaled haplotypic density analyses suggested comparable success indices between lineage 2 and lineage 4 (P = 0.306), and drug resistance did not significantly promote the transmission of Mtb isolates (P = 0.340). But for pre-XDR isolates, we found a higher success index in those with compensatory mutations (P = 0.025). Mutations under positive selection were found in genes associated with resistance to second-line injectables (whiB6) and drug tolerance (prpR) in both lineage 2 and lineage 4. CONCLUSIONS: Our study demonstrates the population expansion of lineage 2 and lineage 4 in eastern China, with comparable transmission capacity, while accumulation of resistance mutations does not necessarily facilitate the success of Mtb isolates. Compensatory mutations usually accompany drug resistance and significantly contribute to the epidemiological transmission of pre-XDR strains. Prospective molecular surveillance is required to further monitor the emergence and spread of pre-XDR/XDR strains in eastern China.

Quantitative analysis of a novel DNA hypermethylation panel using bronchial specimen for lung cancer diagnosis.

Non-diagnostic findings are common in transbronchial lung biopsy (TBLB) and endobronchial ultrasound-guided transbronchial lung biopsy (EBUS-TBLB). One of the challenges is to improve the detection of lung cancer using these techniques. To address this issue, we utilized an 850 K methylation chip to identify methylation sites that distinguish malignant from benign lung nodules. Our study found that a combination of HOXA7, SHOX2 and SCT methylation analysis has the best diagnostic yield in bronchial washing (sensitivity: 74.1%; AUC: 0.851) and brushing samples (sensitivity: 86.1%; AUC: 0.915). We developed a kit comprising these three genes and validated it in 329 unique bronchial washing samples, 397 unique brushing samples and 179 unique patients with both washing and brushing samples. The panel's accuracy in lung cancer diagnosis was 86.9%, 91.2% and 95% in bronchial washing, brushing and washing + brushing samples, respectively. When combined with cytology, rapid on-site evaluation (ROSE), and histology, the panel's sensitivity in lung cancer diagnosis was 90.8% and 95.8% in bronchial washing and brushing samples, respectively, and 100% in washing + brushing samples. Our findings suggest that quantitative analysis of the three-gene panel can improve the diagnosis of lung cancer using bronchoscopy.

SHOU4/4L link cell wall cellulose synthesis to pattern-triggered immunity.

Pattern recognition receptors (PRRs) are plasma membrane-localised proteins that sense molecular patterns to initiate pattern-triggered immunity (PTI). Receptor-like cytoplasmic kinases (RLCKs) function downstream of PRRs to propagate signal transduction via the phosphorylation of substrate proteins. The identification and characterisation of RLCK-regulated substrate proteins are critical for our understanding of plant immunity. We showed that SHOU4 and SHOU4L are rapidly phosphorylated upon various patterns elicitation and are indispensable for plant resistance to bacterial and fungal pathogens. Protein-protein interaction and phosphoproteomic analysis revealed that BOTRYTIS-INDUCED KINASE 1, a prominent protein kinase of RLCK subfamily VII (RLCK-VII), interacted with SHOU4/4L and phosphorylated multiple serine residues on SHOU4L N-terminus upon pattern flg22 treatment. Neither phospho-dead nor phospho-mimic SHOU4L variants complemented pathogen resistance and plant development defect of the loss-of-function mutant, suggesting that reversible phosphorylation of SHOU4L is critical to plant immunity and plant development. Co-immunoprecipitation data revealed that flg22 induced SHOU4L dissociation from cellulose synthase 1 (CESA1) and that a phospho-mimic SHOU4L variant inhibited the interaction between SHOU4L and CESA1, indicating the link between SHOU4L-mediated cellulose synthesis and plant immunity. This study thus identified SHOU4/4L as new components of PTI and preliminarily revealed the mechanism governing SHOU4L regulation by RLCKs.

Effects of contact tracing and nucleic acid testing on the COVID-19 outbreak in Zunyi, China: data-driven study using a branching process model.

BACKGROUND: During October 2021, China experienced localized outbreaks of COVID-19 in many cities. We analyzed the small local outbreak in Zunyi (Guizhou Province), a major city in southwestern China, and modeled the effects of different interventions on this outbreak. METHODS: Data on infections and contacts, provided by the Health Commission of Guizhou Province, were used to analyze the epidemiological characteristics of the outbreak and calculate the effectiveness of vaccination. A branching process model was used to simulate the outbreak. This model considered the time interval from exposure of the initial case to confirmation, the number of potential infections caused by the initial case, and the effects of the different interventions. RESULTS: From 18 to 25 October 2021, there were 12 patients with COVID-19 in Zunyi. Overall, the average age was 67.17 years-old, 8 patients were females, and 1 patient had an asymptomatic infection. The effectiveness of two-dose inactivated vaccine against SARS-CoV-2 infection was 16.7% (95% CI: 2.8% to 99.7%). The initial case was infected on 11 or 12 October 2021, 6.40 (95% CI: 6.37, 6.42; IQR: 4.92, 7.63) days before confirmation while the travelling in Lanzhou (Gansu Province). There were 10.07 (95% CI: 10.04, 10.09; IQR: 7.86, 11.93) potential secondary cases. When the effective vaccine coverage reached 60%, the probability of cumulative cases exceeding 20 was less than 8.77%, even if contact tracing was relaxed or eliminated. However, if the probability of tracing contacts decreased, earlier initiation of nucleic acid testing was necessary to control the outbreak. CONCLUSIONS: The COVID-19 outbreak in Zunyi was controlled quickly due to moderately effective vaccine coverage and rapid contact tracing. For controlling localized outbreaks, vaccination and contact tracing seemed to be more effective than massive nucleic acid testing in the initial phase of transmission. However, if there is low effective vaccine coverage or insufficient contact tracing, nucleic acid testing should start earlier.

Age-specific transmission dynamics under suppression control measures during SARS-CoV-2 Omicron BA.2 epidemic.

BACKGROUND: From March to June 2022, an Omicron BA.2 epidemic occurred in Shanghai. We aimed to better understand the transmission dynamics and identify age-specific transmission characteristics for the epidemic. METHODS: Data on COVID-19 cases were collected from the Shanghai Municipal Health Commission during the period from 20th February to 1st June. The effective reproductive number (Rt) and transmission distance between cases were calculated. An age-structured SEIR model with social contact patterns was developed to reconstruct the transmission dynamics and evaluate age-specific transmission characteristics. Least square method was used to calibrate the model. Basic reproduction number (R0) was estimated with next generation matrix. RESULTS: R0 of Omicron variant was 7.9 (95% CI: 7.4 to 8.4). With strict interventions, Rt had dropped quickly from 3.6 (95% CI: 2.7 to 4.7) on 4th March to below 1 on 18th April. The mean transmission distance of the Omicron epidemic in Shanghai was 13.4 km (95% CI: 11.1 to 15.8 km), which was threefold longer compared with that of epidemic caused by the wild-type virus in Wuhan, China. The model estimated that there would have been a total 870,845 (95% CI: 815,400 to 926,289) cases for the epidemic from 20th February to 15th June, and 27.7% (95% CI: 24.4% to 30.9%) cases would have been unascertained. People aged 50-59 years had the highest transmission risk 0.216 (95% CI: 0.210 to 0.222), and the highest secondary attack rate (47.62%, 95% CI: 38.71% to 56.53%). CONCLUSIONS: The Omicron variant spread more quickly and widely than other variants and resulted in about one third cases unascertained for the recent outbreak in Shanghai. Prioritizing isolation and screening of people aged 40-59 might suppress the epidemic more effectively. Routine surveillance among people aged 40-59 years could also provide insight into the stage of the epidemic and the timely detection of new variants. TRIAL REGISTRATION: We did not involve clinical trial.

Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox.

This paper introduces a fault diagnosis method for mine scraper conveyor gearbox gears using motor current signature analysis (MCSA). This approach solves problems related to gear fault characteristics that are affected by coal flow load and power frequency, which are difficult to extract efficiently. A fault diagnosis method is proposed based on variational mode decomposition (VMD)-Hilbert spectrum and ShuffleNet-V2. Firstly, the gear current signal is decomposed into a series of intrinsic mode functions (IMF) by using VMD, and the sensitive parameters of VMD are optimized by using a genetic algorithm (GA). The Sensitive IMF algorithm judges the modal function sensitive to fault information after VMD processing. By analyzing the local Hilbert instantaneous energy spectrum for fault-sensitive IMF, an accurate expression of signal energy changing with time is obtained to generate the local Hilbert immediate energy spectrum dataset of different fault gears. Finally, ShuffleNet-V2 is used to identify the gear fault state. The experimental results show that the accuracy of the ShuffleNet-V2 neural network is 91.66% after 778 s.

CTSB+ macrophage repress memory immune hub in the liver metastasis site of colorectal cancer patient revealed by multi-omics analysis.

Colorectal cancer (CRC) is a lethal malignant tumor and 25-30% of CRC patients develop liver metastasis (LM) with a worse prognosis, but the metastasis mechanism is yet elucidated. To identify the potential immune regulatory mechanism of CRC liver metastasis, single-cell sequencing and multiplex immunohistochemistry were applied to identify key cell populations of the tumor microenvironment (TME) in the CRC and LM sites. We found memory CD8+ T cells, B cells, and CTSB + macrophages were enriched in the LM site, forming the memory immune hub, which was important for the anti-tumor response against LM. Therefore, our results revealed that memory immune responses were called in the LM sites and probably meditated by CTSB + macrophages.

Effectiveness of inactivated and Ad5-nCoV COVID-19 vaccines against SARS-CoV-2 Omicron BA. 2 variant infection, severe illness, and death.

BACKGROUND: Limited data are available on the effectiveness of inactivated and Ad5-nCoV COVID-19 vaccines in real-world use-especially against Omicron variants in SARS-CoV-2 infection-naïve population. METHODS: A matched case-control study was conducted among people aged ≥ 3 years between 2 December 2021 and 13 May 2022. Cases were SARS-CoV-2-infected individuals, individuals with severe/critical COVID-19, or COVID-19-related deaths. Controls were selected from consecutively test-negative individuals at the same time as cases were diagnosed and were exact-matched on year-of-age, gender, birthplace, illness onset date, and residential district in ratios of 1:1 with infected individuals and 4:1 with severe/critical COVID-19 and COVID-19-related death. Additionally, two subsets were constructed to analyze separate vaccine effectiveness (VE) of inactivated vaccines (subset 1) and Ad5-vectored vaccine (subset 2) against each of the three outcomes. RESULTS: Our study included 612,597 documented SARS-CoV-2 infections, among which 1485 progressed to severe or critical illness and 568 died. Administering COVID-19 vaccines provided limited protection against SARS-CoV-2 infection across all age groups (overall VE: 16.0%, 95% CI: 15.1-17.0%) but high protection against severe/critical illness (88.6%, 85.8-90.8%) and COVID-19-related death (91.6%, 86.8-94.6%). In subset 1, inactivated vaccine showed 16.3% (15.4-17.2%) effective against infection, 88.6% (85.8-90.9%) effective against severe/critical COVIID-19, and 91.7% (86.9-94.7%) against COVID-19 death. Booster vaccination with inactivated vaccines enhanced protection against severe COVID-19 (92.7%, 90.1-94.6%) and COVID-19 death (95.9%, 91.4-98.1%). Inactivated VE against infection began to wane 12 weeks after the last dose, but two and three doses sustained high protection levels (> 80%) against severe/critical illness and death, while subset 2 showed Ad5-vectored vaccine was 13.2% (10.9-15.5%) effective against infection and 77.9% (15.6-94.2%) effective against severe/critical COVIID-19. CONCLUSIONS: Our real-world study found high and durable two- and three-dose inactivated VE against Omicron-associated severe/critical illness and death across all age groups, but lower effectiveness against Omicron infection, which reinforces the critical importance of full-series vaccination and timely booster dose administration for all eligible individuals.

CCIP: predicting CTCF-mediated chromatin loops with transitivity.

MOTIVATION: CTCF-mediated chromatin loops underlie the formation of topological associating domains and serve as the structural basis for transcriptional regulation. However, the formation mechanism of these loops remains unclear, and the genome-wide mapping of these loops is costly and difficult. Motivated by the recent studies on the formation mechanism of CTCF-mediated loops, we studied the possibility of making use of transitivity-related information of interacting CTCF anchors to predict CTCF loops computationally. In this context, transitivity arises when two CTCF anchors interact with the same third anchor by the loop extrusion mechanism and bring themselves close to each other spatially to form an indirect loop. RESULTS: To determine whether transitivity is informative for predicting CTCF loops and to obtain an accurate and low-cost predicting method, we proposed a two-stage random-forest-based machine learning method, CTCF-mediated Chromatin Interaction Prediction (CCIP), to predict CTCF-mediated chromatin loops. Our two-stage learning approach makes it possible for us to train a prediction model by taking advantage of transitivity-related information as well as functional genome data and genomic data. Experimental studies showed that our method predicts CTCF-mediated loops more accurately than other methods and that transitivity, when used as a properly defined attribute, is informative for predicting CTCF loops. Furthermore, we found that transitivity explains the formation of tandem CTCF loops and facilitates enhancer-promoter interactions. Our work contributes to the understanding of the formation mechanism and function of CTCF-mediated chromatin loops. AVAILABILITY AND IMPLEMENTATION: The source code of CCIP can be accessed at: https://github.com/GaoLabXDU/CCIP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effect of different interventions for latent tuberculosis infections in China: a model-based study.

BACKGROUND: Tuberculosis (TB) has a serious impact on people's health. China is one of 30 countries that has a high TB burden. As the currently decreasing speed of the incidence of TB, the WHO's goal of "End TB Strategy" is hard to achieve by 2035. As a result, a SEIR model that determines the impact of different tuberculosis preventive treatments (TPTs) in different age groups, and the effect of different interventions on latent TB infections (LTBIs) in China is developed. METHODS: A Susceptible-Exposed-Infectious-Recovered (SEIR) model was established. Goodness-of-fit tests were used to assess model performance. Predictive analysis was used to assess the effect of different interventions on LTBIs and achieving the goals of the "End TB Strategy". RESULTS: The Chi-square test indicated the model provided a good statistical fit to previous data on the incidence of TB (χ2 = 0.3085, p > 0.999). The 1HP treatment regimen (daily rifapentine + isoniazid for 4 weeks) was most effective in reducing the number of TB cases by 2035. The model indicated that several strategies could achieve the 2035 target of the "End TB Strategy": completion of active case finding (ACF) for LTBI and TPT nation-wide within 5 years; completion of ACF for LTBIs and TPT within 2 years in high-incidence areas; completion of TPT in the elderly within 2 years; or introduction of a new vaccine in which the product of annual doses and vaccine efficiency in the three age groups above 14 years old reached 10.5 million. CONCLUSION: The incidence of TB in China declined gradually from 2005 to 2019. Implementation of ACF for LTBIs and TPT nation-wide or in areas with high incidence, in the elderly, or administration of a new and effective vaccine could greatly reduce the number of TB cases and achieve the 2035 target of the "End TB Strategy" in China.

Progression of severity in coronavirus disease 2019 patients before treatment and a self-assessment scale to predict disease severity.

OBJECTIVES: This study aims to further investigate the association of COVID-19 disease severity with numerous patient characteristics, and to develop a convenient severity prediction scale for use in self-assessment at home or in preliminary screening in community healthcare settings. SETTING AND PARTICIPANTS: Data from 45,450 patients infected with COVID-19 from January 1 to February 27, 2020 were extracted from the municipal Notifiable Disease Report System in Wuhan, China. PRIMARY AND SECONDARY OUTCOME MEASURES: We categorized COVID-19 disease severity, based on The Chinese Diagnosis and Treatment Protocol for COVID-19, as "nonsevere" (which grouped asymptomatic, mild, and ordinary disease) versus "severe" (grouping severe and critical illness). RESULTS: Twelve scale items-age, gender, illness duration, dyspnea, shortness of breath (clinical evidence of altered breathing), hypertension, pulmonary disease, diabetes, cardio/cerebrovascular disease, number of comorbidities, neutrophil percentage, and lymphocyte percentage-were identified and showed good predictive ability (area under the curve = 0·72). After excluding the community healthcare laboratory parameters, the remaining model (the final self-assessment scale) showed similar area under the curve (= 0·71). CONCLUSIONS: Our COVID-19 severity self-assessment scale can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance. The tool is also practical for use in preliminary screening in community healthcare settings. Our study constructed a COVID-19 severity self-assessment scale that can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance.

Whole-genome sequencing of Mycobacterium tuberculosis for prediction of drug resistance.

Whole-genome sequencing (WGS) has shown tremendous potential in rapid diagnosis of drug-resistant tuberculosis (TB). In the current study, we performed WGS on drug-resistant Mycobacterium tuberculosis isolates obtained from Shanghai (n = 137) and Russia (n = 78). We aimed to characterise the underlying and high-frequency novel drug-resistance-conferring mutations, and also create valuable combinations of resistance mutations with high predictive sensitivity to predict multidrug- and extensively drug-resistant tuberculosis (MDR/XDR-TB) phenotype using a bootstrap method. Most strains belonged to L2.2, L4.2, L4.4, L4.5 and L4.8 lineages. We found that WGS could predict 82.07% of phenotypically drug-resistant domestic strains. The prediction sensitivity for rifampicin (RIF), isoniazid (INH), ethambutol (EMB), streptomycin (STR), ofloxacin (OFL), amikacin (AMK) and capreomycin (CAP) was 79.71%, 86.30%, 76.47%, 88.37%, 83.33%, 70.00% and 70.00%, respectively. The mutation combination with the highest sensitivity for MDR prediction was rpoB S450L + rpoB H445A/P + katG S315T + inhA I21T + inhA S94A, with a sensitivity of 92.17% (0.8615, 0.9646), and the mutation combination with highest sensitivity for XDR prediction was rpoB S450L + katG S315T + gyrA D94G + rrs A1401G, with a sensitivity of 92.86% (0.8158, 0.9796). The molecular information presented here will be of particular value for the rapid clinical detection of MDR- and XDR-TB isolates through laboratory diagnosis.

Quantitative assessment of the effects of massive nucleic acid testing in controlling a COVID-19 outbreak.

BACKGROUND: From 20 July to 26 August 2021, local outbreaks of COVID-19 occurred in Nanjing City and Yangzhou City (Jiangsu Province, China). We analyzed the characteristics of these outbreaks in an effort to develop specific and effective intervention strategies. METHODS: Publicly available data on the characteristics of the COVID-19 outbreaks in Jiangsu Province were collected. Logistic regression was used to assess the association of age and sex with clinical severity. Analysis of onset dates, generation time distributions, and locations were used to estimate the mean transmission distance. A branching process model was used to evaluate different management strategies. RESULTS: From 20 July to 26 August 2021, 820 patients were diagnosed with COVID-19 in Jiangsu Province, with 235 patients (28.7%) from Nanjing, 570 (69.5%) from Yangzhou, and 15 (1.8%) from other cities. Overall, 57.9% of the patients were female, 13.7% were under 20 years-old, and 58.3% had moderate disease status. The mean transmission distance was 4.12 km, and closed-loop management of the area within 2.23 km of cases seemed sufficient to control an outbreak. The model predicted that the cumulative cases in Yangzhou would increase from 311 to 642 if the interval between rounds of nucleic acid amplification testing (NAAT) increased from 1 to 6 days. It also predicted there would be 44.7% more patients if the NAAT started 10 days (rather than 0 days) after diagnosis of the first case. The proportion of cases detected by NAAT would increase from 11.16 to 44.12% when the rounds of NAAT increased from 1 to 7 within 17 days. When the effective vaccine coverage was 50%, the outbreak would be controlled even when using the most relaxed non-pharmaceutical interventions. CONCLUSIONS: The model predicted that a timely closed-loop management of a 2.23 km area around positive COVID-19 cases was sufficient to control the outbreak. Prompt serial NAAT is likely to contain an outbreak quickly, and our model results indicated that three rounds of NAAT sufficiently controlled local transmission. Trial registration We did not involve clinical trial.

Long-term immune responses in patients with confirmed novel coronavirus disease-2019: a 9-month prospective cohort study in Shanghai, China.

BACKGROUND: The duration of antibodies against SARS-CoV-2 in Covid-19 patients remains uncertain. Longitudinal serological studies are needed to prevent disease and transmission of the virus. METHODS: In 2020, 414 blood samples were tested, obtained from 157 confirmed Covid-19 patients, in a prospective cohort study in Shanghai. RESULTS: The seropositive rate of IgM peaked at 40.5% (17/42) within 1 month after illness onset and then declined. The seropositive rate of IgG was 90.6% (58/64) after 2 months, remained above 85% from 2 to 9 months and was 90.9% (40/44) after 9 months. Generalized estimating equations models suggested that IgM (P < 0.001) but not IgG significantly decreased over time. Age ≥ 40 years (adjusted odds ratio [aOR] 4.531; 95% confidence interval [CI] 1.879-10.932), and cigarette smoking (aOR 0.344; 95% CI 0.124-0.951) were associated with IgG, and age ≥ 40 years (aOR 2.820; 95% CI 1.579-5.036) was associated with IgM. After seroconversion, over 90% and 75.1% of subjects were estimated to remain IgG-positive 220 and 254 days, respectively. Of 1420 self-reported symptoms questionnaires, only 5% reported symptoms 9 months after onset. CONCLUSIONS: In patients with a history of natural infection, anti-SARS-CoV-2 IgG is long-lived, being present for at least 9 months after illness onset. The long duration of natural immunity can mitigate and eliminate Covid-19 and the ongoing pandemic.