Information Database Research Articles

NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage

Objective. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters. Approach. A literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation. Main Results. We compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%. Significance. This novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.

Therapy-related myeloid neoplasm risk score (TMNRS): A convenient clinical score for TMN risk assessment at presentation in adult patients (pts) with cancer.

11164 Background: tMNs are rare, albeit serious complication in cancer survivors. A tMN risk prediction model is currently not available, therefore we leveraged clinical information and history in a large population-claims database to devise the first tMN risk prediction model in adult cancer survivors. Methods: SEER-Medicare was used to analyze 970,390 adults aged 66-84 diagnosed with first primary cancer (FPC) from 2000-2011 (with follow-up through 2015), who survived ≥1 year. Medicare claims data classified individuals with a priori identified conditions. We focused on prostate, GI, breast, lung, and bladder (n=830,468) FPCs to develop a tMN risk prediction model. The population was divided into training and validation cohorts. Χ2 test identified risk factors associated with tMN. The tMN risk score (TMNRS) was created as a simple arithmetic sum of independent predictors of tMN weighted according to the adjusted odds ratio from logistic regression analysis. Pts were categorized into risk groups (Table) based on their TMNRS, which were tested in the validation cohort. Model performance was evaluated using ROC and c-statistics. Results: 87-96% of tMN developed within the first 10 years of FPC. Predictors of tMN included history of autoimmunity, infections, cardiovascular disease, granulocyte-colony stimulating factor, FPC stage, chemoradiotherapy exposure, age at FPC diagnosis, and sex. Training analysis distinguished survivors into distinct risk groups (table) with 10-year incidence of tMN ranging from 0.3% to 1.5% (prostate), 0.04% to 1.1% (GI), 0.2% to 1.4% (breast), 0.1% to 1.2% (lung) and 0.1% to 1.4% (bladder). The table summarizes the incidence of tMN and by risk category for each of the FPCs. After validation, the c-statistic ranged from 0.62-0.68. Conclusions: We utilized the largest SEER-Medicare cohort to date to develop a tMN risk model. The TMNRS captures several novel predictors and can be readily used in the clinic. This easy-to-use tool distinguishes cancer survivors into clinically relevant groups at risk of tMN development. This study lays the framework for screening and monitoring of pts at high risk of tMN. In future, we will leverage increasingly available molecular data to improve prediction performance of the model. Web-based calculator to be published with the manuscript/available at time of presentation. [Table: see text]

Cytogenetic and molecular features of mixed-phenotype acute leukemia in US veterans and its potential impact on therapy.

e18535 Background: Mixed phenotype acute leukemia (MPAL) is a rare and heterogeneous subtype of acute leukemia. Per the 2022 WHO criteria, it is categorized based on its predominant immunophenotypic lineage (B vs. T) and the presence of a Philadelphia chromosome (Ph+) or a KMT2A rearrangement. While many other cytogenetic and molecular abnormalities have been described, due to the rarity of this disease, neither their incidence nor the role of targeted therapies has been well defined. We conducted a retrospective study of all veterans with MPAL diagnosed between 2000-2023 to examine the impact of different clinical, immunophenotypic, cytogenetic, molecular, and treatment strategies on overall survival (OS). Methods: Electronic medical record data from the Veterans Affairs Informatics and Computing Infrastructure database were used to identify 320 patients diagnosed with MPAL between 2000-2023 using the text utilization integration feature to query all notes. All patient charts were reviewed manually. Pathology reports and clinical notes were reviewed to collect data on diagnosis, treatment, and outcome. To date, 74 patients have been identified and included in this preliminary analysis. Cox regression analyses were used to compare OS between patients with different MPAL subtypes, cytogenetic, and molecular features as well as different treatment approaches. Multivariate analysis was performed with controls for age, race, and BMI. Results: In line with prior research, decreases in OS were seen among older patients > 65 yo compared to patients <30 yo and those who received an AML induction over ALL induction regimen with HR of 8.28 (p 0.04) and 2.02 (p 0.03) respectively. Patients who had a transplant had significantly improved OS with a HR of 0.19 (p <0.001). Of the 32 patients who had potentially targetable mutations, 14 received targeted maintenance therapy with either Ph+ or FLT3 inhibitors. Patients who got targeted maintenance therapy (including FLT3 inhibitors) had an OS of 55 months compared to 17 months for those who did not. 50% of those patients who did not get maintenance therapy went to transplant and 31% of patients who did get maintenance therapy went to transplant yet these patients still had improved OS. Conclusions: This preliminary data demonstrate that while MPAL is a complex and heterogeneous disease, a significant portion of patients have targetable mutations. Further studies evaluating targeted therapies such as FLT3, IDH, and menin inhibitors in MPAL patients may be warranted. [Table: see text]

Electronic Surveillance in Immigration Court: Evidence from the CalGang Database

Through a mixed-methods approach that draws on over 4000 pages of organizational documents and two quantitative data sets from federal and state entities, the authors ask, has the growth of electronic surveillance by local criminal justice agencies, with corresponding database interoperability and information sharing, changed how federal agencies—specifically immigration agencies—operate? We focus on the CalGang Database, California’s statewide gang intelligence database, to document how, since its inception in 1998, CalGang has grown both in the number of individuals under surveillance and the number of what we call adjacent users, which we define as people who can access CalGang data but were not initially intended as users and who, in pre-digital surveillance eras, would have a much more difficult time obtaining the information. In this paper we focus specifically on adjacent users in the immigration system who determine the detention and deportation of noncitizens by leveraging an information sharing agreement that gave U.S. Immigration and Customs Enforcement (ICE) access to Calgang between January 2006 and October 2016. Using a two-way “difference in differences” approach, we estimate the impact of CalGang interoperability on federal adjacent users by comparing outcomes across immigration cases that began before or after the data sharing agreement ended. Our qualitative and quantitative analyses both demonstrate that immigration adjudicators’ access to local electronic surveillance information is associated with an increase in the number of immigrants facing criminal immigration charges that do not necessarily require a formal criminal conviction in state court, and that deportation rates are higher when ICE has access to local electronic surveillance.

AI-generated synthetic clinical-genomic data for precision oncology research: Validation using a case study on lung adenocarcinoma.

e13627 Background: The analysis of genomic variants is crucial in precision oncology research, offering insights into cancer risks and progression, especially in diverse types such as lung adenocarcinoma (LUAD). However, such research often grapples with balancing patient privacy with the need for comprehensive, high-quality genomic datasets. Our project addresses this by creating synthetic clinical-genomic data, which maintains patient confidentiality and provides a rich resource for genomic cancer research. Methods: Leveraging the GuardantINFORM database, which includes anonymized genomic data and structured payer claims, we focused on generating synthetic data for LUAD patient cohorts. This approach involves processing real patient data into a format compatible with Medisyn’s generative AI models, ensuring the synthetic data retains the original's statistical properties, and processing the output back into the original database structure and format. This method plays a crucial role in maintaining patient privacy and serves as a valuable tool for research by enabling the generation of realistic patients with desired properties on demand. Results: Our synthetic data closely mirrors real-world genomic and claims variable distributions, evidenced by a 0.994 R2 correlation between real and synthetic data along with comparable Oncoprints. Importantly, privacy tests show that patient confidentiality is effectively maintained despite this effective performance. The synthetic data's utility was then demonstrated in a study replicating real-world findings: LUAD patients with KRAS G12C in combination with STK11 mutations showed a significantly higher risk of early mortality. This underscores the potential of synthetic data in advancing cancer research. Conclusions: This research offers a promising avenue for the cancer research community. By providing a method to share privatized, synthetic genomic data, which can be combined and generated on demand, we enable broader, more responsible data sharing. This approach protects patient privacy and offers a rich dataset for groundbreaking research, potentially accelerating advances in cancer diagnosis and treatment. [Table: see text]

Trends in malignant pleural mesothelioma prevalence in the United States from 2000 to 2020.

e20094 Background: Malignant pleural mesothelioma (MPM) is a rare cancer that is closely associated with prolonged asbestos exposure. Its incidence has steadily declined over time with decreasing prevalence of asbestos-related lung disease. Our study aims to provide an update on recent MPM trends in the United States from 2000 to 2020. Methods: This is a retrospective cohort study design. The primary endpoints of this study are age-adjusted MPM rates and annual percentage change (APC) in rates from 2000-2020. We queried the Surveillance, Epidemiology, and End Results (SEER) 17 database for information about demographics, cause of death, and survival time for patients diagnosed with MPM from 2000-2020. For rate analysis, we used the SEER 22 database which covers 22 national cancer registries and approximately 41.9% of the US population. Age-adjusted rate analysis was performed using the National Cancer Institute (NCI) joinpoint regression program. Results: We identified 13,018 patients diagnosed with MPM from 2000-2020 who were eligible for analysis in the SEER 17 database. Individuals with MPM were primarily male (78.2%) and belonged to Non-Hispanic White (80.7%) ancestry. The mean age at diagnosis overall was 72.4 (SD +/- 10.6) years. The overall age-adjusted rate of MPM for all groups decreased from 0.97 (cases per 100,000 population) in 2000 to 0.55 in 2020. When stratified by sex, the MPM rate in males decreased from 1.91 to 0.93. The rate decrease in females over the same period was more modest, from 0.33 to 0.27. Overall APC for all groups from 2000-2013 was -1.45 (95% CI -2.01 to -0.40, p<.05), and from 2013-2020 it was -5.22 (95% CI -8.92 to -3.76, p<.05). The APC for males from 2000-2013 was -1.88 (95% CI -2.62 to -0.62, p<.05) and -6.46 (95% CI -10.49 to -4.57, p<.05) from 2013-2020. The rate decrease for females demonstrated a consistent APC of -0.87 (95% CI -1.54 to -0.26, p<.05) from 2000-2020. Conclusions: MPM is a disease that is best managed at large academic cancer centers due to its rarity. Our study demonstrates an accelerated decline in overall MPM rates since 2013, mainly driven by a similar fall in rates in males over the same period. MPM rates are lower in females and continue to decline, albeit slowly. The continued and reassuring decrease in rates of MPM likely reflects the effectiveness of regulations targeting widespread industrial and residential asbestos use that have been in place since the 1990s. [Table: see text]

Optimal neoadjuvant treatment and prognostic factors in patients with HR-positive/HER2-positive early or locally advanced breast cancer: A national real-world study in China.

e12514 Background: Few data have been reported regarding breast cancer (BC) population characterized by HR+/HER2+ especially in a real-world setting in China. This analysis aimed at identifying differences in short-term treatment outcomes of different neoadjuvant therapy (NAT) regimens, effects of the demographics and clinical features on short-term outcomes of NAT, and further understanding the prognostic factors related to those outcomes in HR+/HER2+ early (EBC) or locally advanced BC (LABC) pts by using dataset from National Cancer Information Database (NCID) in China. Methods: This retrospective, multicenter, real-world study used electronic medical record data from NCID. We analyzed HR+/HER2+ EBC or LABC Chinese pts who were initially diagnosed between Jan 1, 2019 and May 31, 2022 and received NAT followed by surgery during study period. Primary endpoints included total pathological complete response (tpCR) and breast pCR (bpCR). Secondary endpoints included disease-free survival (DFS) and event-free survival (EFS). Results: 3792 pts from NCID covering 51 hospitals, 26 provinces met inclusion criteria and were included. After controlling for age and post-surgery N staging, 1-, 2- and 3-yr DFS was similar between trastuzumab (T) and T+pertuzumab (P) NAT subgroup (92.79%, 87.20% and 83.32% vs 94.83%, 88.82% and 87.88%; p=0.1120). 1-, 2- and 3-yr EFS was higher in T+P subgroup (T vs T+P: 85.24%, 82.35%, 77.01% vs 90.86%, 89.65%, 82.78%; p = 0.0003). Logistic regression multivariate analysis showed improved tpCR and bpCR was independently positively associated with receiving T+P NAT (vs T: both p < 0.0001), platinum NAT (vs anthracycline: p = 0.0071 and 0.0122, resp.), while negatively associated with receiving no targeted therapy (vs T: p = 0.0002 and < 0.0001, resp.), ≥ 65 yrs (vs > 40 yrs, < 64 yrs: p = 0.0116 and 0.0290, resp.), or post-surgery ER positive (vs negative: both p < 0.0001). Conclusions: HR+/HER2+ EBC or LABC gained short- and long-term benefit from HER2 dual-blockade therapy in clinical practice. Prognostic factors for improved tpCR and bpCR included T+P and platinum NAT, whereas ≥ 65 yrs and post-surgery ER+ were predictive of decreased tpCR and bpCR. [Table: see text]

National trend in the prevalence and mortality of COPD in South Korea from 2008 to 2017

BackgroundExisting studies on chronic obstructive pulmonary disease (COPD) in Korea lack full population coverage, relying on small sample sizes. Therefore, this study aims to investigate the prevalence and mortality of...

Risk of Cardiovascular Events Associated with Chronic Obstructive Pulmonary Disease and/or Metabolic Syndrome: A Large-Scale Nationwide Population-Based Cohort Study.

Chronic obstructive pulmonary disease (COPD) and metabolic syndrome (MetS) are among the most prevalent conditions that might predispose individuals to life-threatening events. We aimed to examine their associations with cardiovascular (CV) events and mortality using a large-scale population dataset from the National Health Information Database in Korea. This population-based cohort study enrolled adults aged ≥40 years who had undergone more than two health examinations between 2009 and 2011. They were divided into four groups based on the presence of COPD and MetS. Analysis of the outcomes and CV events or deaths was performed from 2014 to 2019. We compared CV event incidence and mortality rates using a multivariate Cox proportional hazards model and Kaplan-Meier curves. Totally, 5,101,810 individuals were included, among whom 3,738,458 (73.3%) had neither COPD nor MetS, 1,193,014 (23.4%) had only MetS, 125,976 (2.5%) had only COPD, and 44,362 (0.9%) had both. The risk of CV events was significantly higher in individuals with both COPD and MetS than in those with either COPD or MetS alone (HRs: 2.4 vs 1.6 and 1.8, respectively; all P <0.001). Similarly, among those with both COPD and MetS, all-cause and CV mortality risks were also elevated (HRs, 2.9 and 3.0, respectively) compared to the risks in those with either COPD (HRs, 2.6 and 2.1, respectively) or MetS (HRs, 1.7 and 2.1, respectively; all P <0.001). The comorbidity of MetS in patients with COPD increases the incidence of CV events and all-cause and cardiovascular mortality rates.

Physical independence and related factors among older adults: a systematic review and meta-analysis

Background: Adopting a physically active lifestyle is advocated as a strategy to prevent loss of physical independence and support healthy aging. This study aimed to evaluate the physical independence and related factors among older adults. Materials and methods: This systematic review and meta-analysis was conducted through electronic databases such as Scopus, PubMed, Web of Science, Iranmedex, and Scientific Information Database from the earliest to 1 April 2022. Two researchers independently extracted information from the studies and evaluated the quality of the studies. The analysis was conducted using CMA program version 3, and each study’s importance was determined based on its inverse variance. Results: Five thousand seven hundred thirty-three older adults participated in this review in six studies. All evaluated studies had high quality. The mean score of physical independence in older adults was 20.07 (SE=0.76) out of 24 (95% CI: 18.58–21.56; I2 =98.573%; P&lt;0.001). Physical activity is very important for physical independence and reduces the risk of physical dependence in older adults. Other factors, such as sex, BMI, age, abnormal performance, timed performance, sufficiently active, muscle function, handgrip strength, lower extremity function, lower body strength, maximal isometric knee extension power, lung function, aerobic endurance, sedentary time, agility, and the prevalence of arthritis, had a significant relationship with physical independence in older adults. Conclusion: Older adults demonstrate favourable levels of physical independence. Notably, physical activity emerges as a significant determinant positively associated with such independence. Thus, policymakers and administrators are encouraged to strategize the creation of conducive environments for walking and exercise among older adults.

Cross-scale analysis for the thermo-hydro-mechanical (THM) effects on the mechanical behaviors of fractured rock: Integrating mesostructure-based DEM modeling and machine learning

The excavation process induces the emergence of damage zones in the on-site engineering rock masses, significantly affecting the parameter evaluation of surrounding rock masses. Therefore, an in-depth understanding of the thermo-hydro-mechanical (THM) effects on the cross-scale failure process of fractured rock becomes progressively crucial in natural energy exploitation projects. In this study, a coupled thermo-hydro-mesostructure-based DEM (T-H-MSBM) model was developed to reconstruct rock microstructures and distinguish the THM responses of varying mineral grains, micro-defects and cracks in fractured granite. Five sets of numerical fractured granite in terms of different damage degrees were generated by restoring the form of excavation damage on site, and a series of compression simulations were conducted on the T-H-MSBMs under coupled temperature (25–250 °C), pore pressure (0–15 MPa) and confining pressure (15 MPa) conditions. Based on the cross-scale failure analysis on the fractured granite during the THM-compression loading process, the interplay between THM treatment and damage degree on the mechanical properties of fractured granite was revealed, and the main mechanisms affecting the varied macro mechanical properties were further discussed. Results indicate that both temperature and pore pressure exert the amplified deteriorating effect on the macro mechanical properties of fractured granite with increasing damage degree, while the temperature dependence becomes significantly more pronounced in the fractured granite with low damage degree. With an increase in the damage degree of fractured rock, the abundance of intra-mineral tensile cracks accelerates the initiation of pore pressure-induced cracks, and the coupled increase in pore pressure and temperature enhances the role of quartz-feldspar cracks in promoting the cracks in quartz. Finally, utilizing the optimized kernel ridge regression (KRR) model employed the grid search method, the correlation information database of rock macro-mechanical strength and THM factors were generated. The entire training time was less than 3 s, with the relative average errors of 0.7 MPa, which demonstrated the robust capability of machine learning method in predicting the coupled THM phenomena spanning multiple scales in geological materials and systems.

Predicting PAM50 subtypes from whole slide images of prostate cancer biopsies.

e17001 Background: Computational analysis of digitized pathological images has been shown to have diagnostic and prognostic applications. Recent research suggests that molecular features, previously defined at the genomic level, might be additionally recognized. In prostate cancer, transcriptomic PAM50 molecular subtypes may correlate with patient response to systemic therapy. Here we investigate whether computational analysis of digital prostate biopsy slides can predict PAM50 category. Methods: Our study utilized digitized images of 704 prostate biopsy slides from 336 patients with prostate cancer at Northwestern Memorial Hospital with Decipher testing (Veracyte, Inc San Diego, CA). All slides analyzed contain carcinoma with the majority being primary Gleason pattern 3 (545 out of 704). Hematoxylin and eosin stained slides were scanned on a Leica GT450 scanner at 40x magnification. PAM50 subtypes were derived from the Decipher Genomic Resource Information Database (GRID). Data were split at the subject level using 80% for training, 10% for validation, and 10% for evaluation, facilitating model development and assessment. We devised a model comprising two decoupled parts: a multi-instance learning model that utilizes a multi-class attention mechanism to explore relevant class-specific information from slides, and a pre-trained network to extract high-dimensional semantic features from the tiles of WSIs. The model trained based on the WSIs of the training set to predict PAM50 subtypes (luminal A, luminal B, and basal). This attention-based training approach allowed the model to capture relevant patterns and information present in the WSIs. Following the training phase, the model was subsequently evaluated on the testing set. Results: Model AUC on the evaluation set was 0.78. Among evaluation set misclassifications, no luminal samples were misclassified as basal subtype. Conclusions: Our investigation provides preliminary results on the ability to predict RNA expression-based subtypes from prostate cancer biopsy histology. This approach may help to preserve tissue, minimize costs, and decrease turnaround time associated with molecular testing in prostate cancer while offering patients with prostate cancer opportunities for precision medicine.

Resilience and related factors in caregivers of adult cancer patients: a systematic review

Background: This systematic review aimed to investigate resilience and its related factors in caregivers of adult patients with cancer. Materials and methods: A systematic search of online electronic databases including Scopus, PubMed, Web of Science, Iranmedex, and Scientific Information Database (SID) was performed using keywords extracted from Medical Subject Headings such as “Psychological Resilience”, “Caregiver”, and “Cancer” from the earliest to 6 June 2023. The quality of the studies included in this review was evaluated using the appraisal tool for cross-sectional studies (AXIS tool). Results: A total of 2735 caregivers of cancer patients participated in 15 studies. The majority of the studies found that caregivers of cancer patients had high levels of resilience. Factors related to the resilience of cancer patients’ caregivers included caregivers’ social support, caregivers’ quality of life, patients’ resilience, caregivers’ family function, patients’ performance, caregivers’ age, caregivers’ health status, caregivers’ self-esteem, caregivers post-traumatic growth, caregivers religious, caregivers hope, caregivers positive affect, patients age, patients social support, patients resilience support, patients quality of life, caregivers’ anxiety, caregivers’ depression, caregivers’ burden, caregivers level of education, caregivers financial problem, caregivers memory, caregivers negative affect, caregivers post-traumatic stress disorder, maternal distress, and patients post-traumatic stress disorder. Conclusion: Therefore, healthcare administrators and policymakers can enhance the resilience of caregivers and the quality of care they provide by instituting ongoing training initiatives focused on evaluating mental well-being and implementing coping strategies for managing stress and depression.

Pilot fatigue evaluation with the method of weight TOPSIS

Abstract Fatigue is significant to many aviation accidents, so assessing pilot fatigue is critical to aviation safety. Traditional fatigue risk assessment studies often ignore the impact of difficult flight maneuvers on pilot fatigue. The study sought to develop a fatigue risk indicator system that could be integrated with airline data systems to achieve a more accurate assessment of pilot fatigue. The questionnaire content was determined by literature collection and methods, and 128 civil aviation pilots were surveyed by questionnaire. The influence of pilot scheduling, workload, working environment and other specific indicators on pilot fatigue was evaluated by Weight TOPSIS analysis method. Then the model is used to evaluate pilot fatigue for practical scheduling problems. The study found that lack of rest time, long working hours and red-eye flights were the most common causes of pilot fatigue. In practice, the fatigue evaluation system can be combined with the database information of the airline to help the airline rationally arrange the flight, minimize the fatigue index and ensure the safety.

Evaluating Machine Learning Techniques for Web Robot Detection

A web robot, also known as a web crawler, is an automated script that systematically browses the World Wide Web in a methodical manner. Web robots are commonly used by search engines to index web pages, gather information, and update search engine databases. They follow hyperlinks from one web page to another, collecting data and information for various purposes such as indexing, data mining, and website monitoring. Web robot detection is the process of identifying and distinguishing between human users and web robots on websites, which are a major source of web traffic. This process is vital to prevent malicious web robots from having a negative effect on web servers’ traffic and their users’ privacy. Companies such as Imperva, Inc. use machine learning models to identify malicious bots and prevent them from having unauthorized access to an organization’s server. To understand the benefit that machine learning models bring to web robot detection, we used pre-published server log data to construct machine learning models on Orange (a Data Mining Software) and Python that can distinguish between malicious and benign web robots. We evaluated the performance of three well-known machine learning algorithms: kNN, neural network, and decision tree. Based on our experimental results, the neural network gains the highest precision and the lowest false-positive and false-negative percentage of web robots. However, the neural network requires more time to generate the desired output.

Bioinformatics analysis of clinical isolates of Escherichia coli from Nigeria and other countries for quinolone and fluoroquinolone resistance

Quinolones were the drugs of choice for the treatment of bacterial infections, especially, infections caused by Gram negative bacteria. Unfortunately, these drugs have been resisted by the microbial agents including Escherichia coli, known to be the leading cause of Urinary Tract Infection (UTI) globally. This study therefore was aimed at detecting all the genes involved in Quinolone resistance by the E. coli pathogen isolated from Nigeria and from other geographical regions, using robust techniques. Twenty-three sequence data files of Escherichia coli from various countries of the world were retrieved from the National Centre for biotechnology Information (NCBI) database and sent for genome assembly for processing of the short reads into long reads. The outcome was config. fasta files which were comprehensively annotated and characterized for genetic functions and mechanisms. A total of 208 antibiotic resistance genes were detected, out of which 27(13.0%) were linked to quinolone resistance and 14(6.7%) to multi- drug resistance. The result of this study significantly implicated many genes in quinolone resistance; notably were the efflux pump genes and their high percentage abundance. We recommend in-depth study of the genes for their expression capabilities, also the structure and features of the efflux pump genes to enable proper redesigning of drugs by integrating anti efflux pump substances that will selectively prevent the expression of the genes for antibiotic resistance, without any harm to the host, or that can destabilize the positive regulation of the operon for antibiotic resistance.

Design of Path-Planning System for Interventional Thermal Ablation of Liver Tumors Based on CT Images.

Aiming at the shortcomings of artificial surgical path planning for the thermal ablation of liver tumors, such as the time-consuming and labor-consuming process, and relying heavily on doctors' puncture experience, an automatic path-planning system for thermal ablation of liver tumors based on CT images is designed and implemented. The system mainly includes three modules: image segmentation and three-dimensional reconstruction, automatic surgical path planning, and image information management. Through organ segmentation and three- dimensional reconstruction based on CT images, the personalized abdominal spatial anatomical structure of patients is obtained, which is convenient for surgical path planning. The weighted summation method based on clinical constraints and the concept of Pareto optimality are used to solve the multi-objective optimization problem, screen the optimal needle entry path, and realize the automatic planning of the thermal ablation path. The image information database was established to store the information related to the surgical path. In the discussion with clinicians, more than 78% of the paths generated by the planning system were considered to be effective, and the efficiency of system path planning is higher than doctors' planning efficiency. After improvement, the system can be used for the planning of the thermal ablation path of a liver tumor and has certain clinical application value.

The Financial Strategy for the Development Of Industry 4.0 in the Countries with Developing Economy

Industry 4.0 incorporates cutting-edge digital technology into the supply chain and manufacturing operations to build a more intelligent, networked system. 4.0, which uses digital technology to automate and simplify processes, is the financial strategy for creating and digitizing the supply chain. Issues confronting the supply chain sector may have an answer thanks to financial and economic development strategy technology. It is a safe, decentralized digital ledger that can transparently and impenetrably record data and transactions. With the use of this technology, a common supply chain information database that is available to everyone engaged in the financial plan for the nation's development can be established. A few advantages of implementing financial and economic development strategy technology in the supply chain sector for financial and economic development strategy are increased traceability and transparency, decreased fraud and counterfeiting risk, faster and more secure payment processing, and enhanced cooperation amongst supply chain participants. Studies on applying financial and economic development strategy technology in supply chains are still scarce. Work presents a thorough step-by-step model based on institutional and resource-based innovation diffusion theory to fill this research vacuum. The model is validated with data from Pakistan and the United Arab Emirates, revealing significant differences between the variables driving both countries' innovation and adoption stages of financial and economic development strategies. The study provides insightful information on how financial and economic development strategies are employed in developing countries' financial strategies.

Research on heritage characteristics based on railway architectural heritage database in Jinzhou section of the Peking–Mukden Railway

This research interprets the background of Jinzhou section of the Peking–Mukden Railway, and puts forward 65 heritages as cases based on the scope definition and investigation. After the data collection, processing, and visualization, the database composed of 9 sub-databases, with B/S architecture mode, is constructed based on SQL server platform. The ArcGIS tool is used to analyze the distribution of the heritages, including spatial distribution characteristics, spatial agglomeration, and spatial equilibrium. “Image and model information database” and “text attribute information database” is used to analyze the architectural ontology features. The conclusions are drawn as follows: 1) The integral distribution has the characteristics of “cohesion”, while the 5 medium types of heritages show obvious and different directions. 2) The overall pattern of spatial agglomeration is characterized by high cohesion with a single high agglomeration point as the core. The low agglomeration area shows a point-line-point pattern. 3) The integral heritages and three main types of buildings differ in distribution, and the equilibrium is low. The architectural ontology analysis shows that the image information can be used as the basis for ontology characteristics analysis, architectural form and style judgment, and architectural functional space analysis.

Организация информационно-аналитического обеспечения оперативно-розыскной деятельности органов внутренних дел по розыску лиц, пропавших без вести, в условиях контртеррористической операции и вооруженных конфликтов

The planning and implementation of operational search activities play a pivotal role in the search for missing persons amidst counter-terrorist operations and armed conflicts. Key to these efforts is the utilization of data that can aid in the identification or recognition of the missing individual. Such data includes physical descriptions in the form of verbal portraits or photographic impressions, basic identifying information, fingerprint cards, and handwriting samples. These pieces of information serve to individualize the sought-after person and can be instrumental in their identification. The establishment of new databases of operational search information, including databases of genomic codes of individuals, could prove to be a valuable tool in obtaining information about unidentified bodies and linking them to missing persons. However, the implementation of such databases raises a number of technical, legal, and ethical issues that must be considered. This article aims to examine the specifics of organizing information and analytical support for the operational activities of internal affairs bodies in the search for missing persons in conditions of counter-terrorist operations and armed conflicts, utilizing available resources accessible to operatives. In conclusion, the effective organization of information and analytical support is paramount in facilitating the search for missing persons within the challenging contexts of counter-terrorist operations and armed conflicts. Leveraging various data sources, including physical descriptions and emerging technologies like genomic databases, offers promising avenues for enhancing identification processes. However, the integration of such resources necessitates careful navigation of technical, legal, and ethical considerations. By addressing these complexities, internal affairs bodies can optimize their operational efforts and contribute significantly to resolving cases of missing individuals amidst complex security environments.