bluuebunny/crossref_metadata_2025_split huggingface dataset:
Culture and sensitivity pattern of aerobic bacterial isolates in diabetic foot infections during 2018–2022 in Asian countries: a literature review study
Authors: Huwae Thomas E.C.J. , Ratridewi Irene , Lena Yulanda M. , Retnoningsih Dewi , Sananta Panji , Asmiragani Syaifullah 
Published: March 2023

Background:The most prevalent consequence of diabetes mellitus is diabetic foot infections (DFIs). Prior to the final treatment established by the culture findings, the early identification of infections may be used as a prescription for an empirical therapy. This study examines the microbiological profile and antimicrobial susceptibility profile of the bacteria that cause DFI.Methods:This research aims to determine the culture and sensitivity trend of aerobic bacterial isolates of DFI in Asian nations over a 5-year period. The article was searched using PubMed and Google Scholar with the keywords ‘Diabetic Foot Infections’, ‘Antibiotic’, ‘Microbiological Profile’, and their combinations. The author uses publications from 2018 to 2022 in Indonesian and English to select the appropriate journal.Results:The author identified 11 relevant articles with microbiological profiles and sensitivity patterns in DFI. A total of 3097 isolates were found in 2498 patients with DFI. Gram-negative bacteria were the leading source of infection (n=1737; 56%). Totally, 1148 (or 37%) of all isolates were aerobic Gram-positive cocci.Staphylococcus aureuswas the most commonly isolated aerobe (n=608, 20%), followed byPseudomonas aeruginosa(n=451, 15%). Gram-positive bacteria showed good susceptibility to trimethoprim-sulfamethoxazole, chloramphenicol, doxycycline, vancomycin, and linezolid. Gram-negative bacteria displayed excellent susceptibility to aminoglycosides, piperacillin-tazobactam, and carbapenems.Conclusions:Gram-negative microorganisms were the most prevalent cause of DFI. This study’s findings will facilitate the development of future empirical therapeutic guidelines for the treatment of DFI.
Abdominal pregnancy: a case report
Authors: Abdullah Lava , Alsulaiman Sihaam S. , Hassan Maher , Ibrahim Hevy S. , Alshamali Nedal , Nizami Sindebad 
Published: March 2023

Introduction and Importance:
Abdominal pregnancy is a rare form of ectopic pregnancies, accounts less than 1% of all types of ectopic pregnancy. Its significance comes from high morbidity and mortality rates.

Case Presentation:
We present a case of a 22-year-old shocked patient with acute abdominal pain underwent laparotomy, abdominal pregnancy implanted into the posterior wall of the uterus diagnosis was made, and appropriate follow-up was offered.

Clinical Discussion:
Acute abdominal pain may be the main symptom of the abdominal pregnancy. The diagnosis was made by direct visualization of the products of conception and pathological study confirmed.

Conclusion:
The first case of abdominal pregnancy is implanted into the posterior wall of the uterus. Follow-up is recommended until β-human chorionic gonadotropin levels become undetectable.
Characterizing OPRM1 DNA methylation in prescription opioid users with chronic musculoskeletal pain
Authors: Sheikh Sophia , Smotherman Carmen , Patel Monika , Langaee Taimour , Wang Danxin , Swaray Edward , Velasquez Esteban , Schmidt Siegfried O.F. , Hendry Phyllis , Cavallari Larisa H. , Fillingim Roger B. 
Published: March 2023

Abstract

Introduction:
Many patients with chronic pain use prescription opioids. Epigenetic modification of the μ-opioid receptor 1 (OPRM1) gene, which codes for the target protein of opioids, may influence vulnerability to opioid abuse and response to opioid pharmacotherapy, potentially affecting pain outcomes.

Objective:
Our objective was to investigate associations of clinical and sociodemographic factors with OPRM1 DNA methylation in patients with chronic musculoskeletal pain on long-term prescription opioids.

Methods:
Sociodemographic variables, survey data (Rapid Estimate of Adult Health Literacy in Medicine-Short Form, Functional Comorbidity Index [FCI], PROMIS 43v2.1 Profile, Opioid Risk Tool, and PROMIS Prescription Pain Medication Misuse), and saliva samples were collected. The genomic DNA extracted from saliva samples were bisulfite converted, amplified by polymerase chain reaction, and processed for OPRM1-targeted DNA methylation analysis on a Pyrosequencing instrument (Qiagen Inc, Valencia, CA). General linear models were used to examine the relationships between the predictors and OPRM1 DNA methylation.

Results:
Data from 112 patients were analyzed. The best-fitted multivariable model indicated, compared with their counterparts, patients with > eighth grade reading level, degenerative disk disease, substance abuse comorbidity, and opioid use < 1 year (compared with >5 years), had average methylation levels that were 7.7% (95% confidence interval [CI] 0.95%, 14.4%), 11.7% (95% CI 2.7%, 21.1%), 21.7% (95% CI 10.7%, 32.5%), and 16.1% (95% CI 3.3%, 28.8%) higher than the reference groups, respectively. Methylation levels were 2.2% (95% CI 0.64%, 3.7%) lower for every 1 unit increase in FCI and greater by 0.45% (95% CI 0.08%, 0.82%) for every fatigue T score unit increase.

Conclusions:

OPRM1 methylation levels varied by several patient factors. Further studies are warranted to replicate these findings and determine potential clinical utility.
Analgesic efficacy of sleep-promoting pharmacotherapy in patients with chronic pain: a systematic review and meta-analysis
Authors: Andersson Emelie , Kander Thomas , Werner Mads U. , Cho Joshua H. , Kosek Eva , Bjurström Martin F. 
Published: March 2023

Abstract
Dysregulation of sleep heightens pain sensitivity and may contribute to pain chronification. Interventions which consolidate and lengthen sleep have the potential to improve pain control. The main objective of this systematic review was to examine the effects of sleep-promoting pharmacotherapy on pain intensity in patients with chronic pain. Multiple electronic databases were searched from inception to January 2022 to identify relevant randomized controlled trials (RCTs). Two independent reviewers screened titles, abstracts, and full-text articles; extracted data; and assessed risk of bias for each included study. The GRADE approach was used to determine the strength of evidence. The search identified 624 articles. After full-text screening, 10 RCTs (n = 574 randomized participants) involving 3 pharmacologic interventions (melatonin, zopiclone, and eszopiclone) and 7 different chronic pain populations were included. Minimum clinically significant pain reduction ≥30% was reported in 4 studies. There is low-quality evidence (downgraded due to inconsistency and imprecision) that 2 to 8 weeks treatment with a sleep-promoting medication alone or in combination with an analgesic (6 trials, n = 397) decreases pain intensity compared with placebo or the same analgesic treatment alone (SMD −0.58 [95% confidence interval −1.00, −0.17], P = 0.006). Analyses of associations between changes in sleep and pain outcomes were only provided in 2 articles, with inconsistent findings. Notably, pain-relieving effects were most consistent in melatonin trials. Only 3 studies implemented polysomnography to obtain objective sleep measures. Low-quality evidence indicates that pharmacologic sleep promotion may decrease pain intensity in chronic pain populations. More research is needed to fully understand the influence of sleep-targeting interventions on pain control.
Artificial Intelligence Modeling to Predict Periprosthetic Infection and Explantation Following Implant-Based Reconstruction
Authors: Hassan Abbas M. , Biaggi-Ondina Andrea , Asaad Malke , Morris Natalie , Liu Jun , Selber Jesse C. , Butler Charles E. 
Published: March 2023

Background:
Despite improvements in prosthesis design and surgical techniques, periprosthetic infection and explantation rates following implant-based reconstruction (IBR) remain relatively high. Artificial intelligence is an extremely powerful predictive tool that involves machine learning (ML) algorithms. We sought to develop, validate, and evaluate the use of ML algorithms to predict complications of IBR.

Methods:
A comprehensive review of patients who underwent IBR from January 2018 to December 2019 was conducted. Nine supervised ML algorithms were developed to predict periprosthetic infection and explantation. Patient data were randomly divided into training (80%) and testing (20%) sets.

Results:
We identified 481 patients (694 reconstructions) with a mean (± SD) age of 50.0 ± 11.5 years, mean (± SD) body mass index of 26.7 ± 4.8 kg/m 2, and median follow-up time of 16.1 months (11.9-23.2 months). Periprosthetic infection developed with 16.3% (n = 113) of the reconstructions, and explantation was required with 11.8% (n = 82) of them. ML demonstrated good discriminatory performance in predicting periprosthetic infection and explantation (area under the receiver operating characteristic curve, 0.73 and 0.78, respectively), and identified 9 and 12 significant predictors of periprosthetic infection and explantation, respectively.

Conclusions:
ML algorithms trained using readily available perioperative clinical data accurately predicts periprosthetic infection and explantation following IBR. Our findings support incorporating ML models into perioperative assessment of patients undergoing IBR to provide data-driven, patient-specific risk assessment to aid individualized patient counseling, shared decision-making, and presurgical optimization.
“Same-Day Mastectomy and Immediate Prosthetic Breast Reconstruction: A Twelve-Year Analysis of a National Database and Early Postoperative Outcomes”
Authors: Chow Amanda L. , Luthringer Margaret M. , Van Kouwenberg Emily A. , Agag Richard L. , Sinkin Jeremy C. 
Published: March 2023

Background:
COVID-19 and rising healthcare costs have incentivized shorter hospital stays after mastectomies with immediate prosthetic reconstruction. The purpose of this study was to compare postoperative outcomes following same-day and non-same day mastectomy with immediate prosthetic reconstruction.

Methods:
A retrospective analysis of the American College of Surgeons National Surgical Quality Improvement Program database from 2007-2019 was performed. Patients who underwent mastectomies and immediate reconstruction with tissue expanders (TE) or implants (DTI) were selected and grouped based on length of stay. Univariate analysis and multivariate regression were performed to compare 30-day postoperative outcomes between length of stay groups.

Results:
A total of 45,451 patients were included: 1,508 had same-day surgery (SDS) and 43,942 were admitted for ≥1 night (non-SDS). There was no significant difference in overall 30-day postoperative complications between SDS and non-SDS following immediate prosthetic reconstruction. SDS was not a predictor of complications (OR 1.1, P=0.346) while TE reconstruction decreased odds of morbidity compared to DTI (OR 0.77, P<0.001). Among patients who had SDS, smoking was significantly associated with early complications on multivariate analysis (OR 1.85, P=0.010).

Conclusions:
Our study provides an up-to-date assessment of the safety of mastectomies with immediate prosthetic breast reconstruction that captures recent advancements. Postoperative complication rates are similar between same-day discharge and at least one-night stay, suggesting that same-day procedures may be safe for appropriately selected patients.
Continuous learning and advancing technologies: a framework for professional development and training in artificial intelligence
Authors: Billiot Theresa 
Published: March 2023

Purpose
The purpose of this article is to assist organizations with professional development and training by using Fink’s (2017) Taxonomy of Significant Learning (TSL) to engage employees with continuous learning, in the context of artificial intelligence (AI). By using TSL, employees engage in six interactive stages that stimulate cognitive and affective learning. This stimulation is highly imperative because employees must acquire and apply new knowledge, develop new attitudes, and change learning behavior to help organizations undergo endless changes shaped by technological advancements.

Design/methodology/approach
This article provides a structure on how to professionally develop and train an organization’s workforce in AI through the six tenets of the TSL model. It also provides guidance on analyzing situational factors and assessing employee learning.

Findings
This conceptual study reveals how organizations can advance employee thinking and shape employee behavior to prepare future leaders during technological shifts.

Practical implications
By integrating the TSL in professional development and training strategies, organizations can achieve employee outcomes that will grow and sustain organizations as technology continues to impact markets.

Originality/value
This paper contributes to existing literature by providing a systematic and quality approach to transform employees into active learners within advanced technological environments. In addition, this paper discusses assessment methods to evaluate the effectiveness of learning.
Iran’s approach to energy policy towards 2040: a participatory scenario method
Authors: Hafezi Reza , Heirani Hossein , Akbari Mohammadreza , Mortezaee Ahmad , Bagherimoghaddam Naser , Heydari Hamid , Souhankar Amirhossein 
Published: March 2023

Purpose
In the case of Iran, about 19 years have passed since the announcement of general energy policies. Policies adopted in the past included too many approaches and goals for the energy sector that some of them are in conflict with each other and their simultaneous implementation was impossible. Therefore, many of the expected goals of these policies have not been achieved. It is very important to adopt an appropriate approach that is compatible with the country’s characteristics and can steer energy policies. Therefore, this study aims to adopt an appropriate approach for the country’s energy sector according to the conditions and needs of the country.

Design/methodology/approach
A participatory scenario technique is designed and used to develop plausible projections in the case of Iran’s energy futures. Based on the proposed methodology, the research process starts with data gathering through interviews and national plans analysis. Then, findings were used as inputs to a simulation-based scenario development process. Scenarios are developed based on Monte Carlo simulation via cross-impact analysis technique purified based on expert judgments.

Findings
In this paper, to recognize Iran’s general approach to energy in the next 20 years, driving forces of change were introduced and used as input for the scenario development phase. Results showed sanctions play a significant role in Iran’s energy future and determine the directions of other driving forces. For renewable energy, it was proposed to increase the renewable share in Iran’s electricity generation mix to 5% of the total installed capacity. In the case of fossil fuel extraction, the maximum efficient rate was proposed for both oil and natural gas national production strategy.

Originality/value
This research is novel both in terms of application and theory. A new participatory scenario development method is used using simulations that are equipped based on experts’ judgments. Also from the practical perspective, this research targeted a future-oriented challenging problem to initialize national policy in a resource-rich developing economy (i.e. Iran).
Servant leadership and employee's job performance: the role of public service motivation in Ethiopian public sector organizations
Authors: Mishra Santap Sanhari , Hassen Molla Hussen 
Published: March 2023

PurposeThis study aims mainly to examine the mediating role of public service motivation (PSM) in the relationship between servant leadership and employee performance in the context of Ethiopian public sector organization employees.Design/methodology/approachA survey design has been used, where all the 154 employees from seven public sector organizations participated. A confirmatory factor analysis employed on the 149 valid responses further validated the hypothesized model.FindingsThe study supported the significant relationship between servant leadership, public service motivation and job performance. Moreover, it exhibited a complete mediating effect of public service motivation, on the relationship between servant leadership and an employee's job performance.Practical implicationsThe successful mediation of PSM into servant leadership and job performance indicates that the new government must focus on empowering colleagues to have a say in the department-level policy formulation process.Originality/valueThe novelty of this study stems from the fact that it is a rare study that investigates the relationship between PSM and servant leadership and job performance in Africa, particularly in Ethiopia, where a regime change in 2018 triggered a flurry of reforms to the public sector work culture.
Compliance concerns in sustainable finance: an analysis of peer-to-peer (P2P) lending platforms and sustainability
Authors: Teichmann Fabian Maximilian Johannes , Boticiu Sonia Ruxandra , Sergi Bruno S. 
Published: March 2023

Purpose
This study aims to analyze the relationship between financial sustainability and peer-to-peer (P2P) lending platforms.

Design/methodology/approach
To do so, an extensive literature review on sustainability, FinTech, P2P lending and their associated risks was conducted using a fundamentally theoretical and descriptive methodology.

Findings
In addition, this study shows that finance can accelerate the transition to a low-carbon circular economy by allocating investments in sustainable projects and businesses. Moreover, FinTech P2P lending platforms can help to vitalize green digital finance by using the internet and information technology in the lending market. Nevertheless, anonymous lending and borrowing ventures can produce potential risks such as money laundering, terrorist financing, fraud risk and information asymmetry.

Originality/value
Sustainable finance remains an emerging and relevant area; however, the literature has not sufficiently addressed compliance concerns. To address this gap, this study aims to contribute to the literature by analyzing the link between sustainable finance and P2P platforms and drawing attention to the compliance risks listed above.
Planning of a distribution network utilizing a heterogeneous fixed fleet with a balanced workload
Authors: Hettiarachchi Punsara , Dharmapriya Subodha , Kulatunga Asela Kumudu 
Published: March 2023

Purpose
This study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature.

Design/methodology/approach
This study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario.

Findings
The solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance.

Research limitations/implications
The developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered.

Originality/value
The planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.
Evolution and simulation of drug safety regulations: construction of a game model for capture event
Authors: Wan Anxia , Huang Qianqian , Elahi Ehsan , Peng Benhong 
Published: March 2023

PurposeThe study focuses on drug safety regulation capture, reveals the inner mechanism and evolutionary characteristics of drug safety regulation capture and provides suggestions for effective regulation by pharmacovigilance.Design/methodology/approachThe article introduces prospect theory into the game strategy analysis of drug safety events, constructs a benefit perception matrix based on psychological perception and analyzes the risk selection strategies and constraints on stable outcomes for both drug companies and drug regulatory authorities. Moreover, simulation was used to analyze the choice of results of different parameters on the game strategy.FindingsThe results found that the system does not have a stable equilibrium strategy under the role of cognitive psychology. The risk transfer coefficient, penalty cost, risk loss, regulatory benefit, regulatory success probability and risk discount coefficient directly acted in the direction of system evolution toward the system stable strategy. There is a critical effect on the behavioral strategies of drug manufacturers and drug supervisors, which exceeds a certain intensity before the behavioral strategies in repeated games tend to stabilize.Originality/valueIn this article, the authors constructed the perceived benefit matrix through the prospect value function to analyze the behavioral evolution game strategies of drug companies and FDA in the regulatory process, and to evaluate the evolution law of each factor.