bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predictors of Outcome After Surgical Decompression for mild degenerative Cervical Myelopathy -A Systematic Review
Authors: Khosravi Sepehr , Farahbakhsh Farzin , Hesari Marjan , Shahmohammadi Alireza , Aliakbargolkar Alireza , Baigi Vali , Eskandari Zahra , Ghodsi Zahra , Harrop James , Rahimi-Movaghar Vafa , Ghodsi Seyed Mohammad 
Published: March 2023

Study Design Systematic Reviews. Objectives To investigate predictors of surgical outcomes for mild Degenerative Cervical Myelopathy (DCM) by reviewing all related studies conducted at this point. Methods An electronic search was carried out in PubMed, EMBASE, Scopus, and Web of Science until June 23, 2021. Full-text articles reporting surgical outcome predictors of mild DCM cases were eligible. We included studies with mild DCM which was defined as a modified Japanese Orthopaedic Association score of 15 to 17 or a Japanese Orthopaedic Association score of 13 to 16. Independent reviewers screened all the records, and discrepancies between the reviewers were solved in a session with the senior author. For risk of bias assessment, RoB 2 tool was used for randomized clinical trials and ROBINS-I for non-randomized studies. Results After screening 6 087 manuscripts, only 8 studies met the inclusion criteria. Lower pre-operative mJOA scores and quality-of-life measurement scores were reported by multiple studies to predict better surgical outcomes compared to other groups. High-intensity pre-operative T2 magnetic resonance imaging (MRI) was also reported to predict poor outcomes. Neck pain before intervention resulted in improved patient-reported outcomes. Two studies also reported motor symptoms prior to surgery as outcome predictors. Conclusion Lower quality of life before surgery, neck pain, lower pre-operative mJOA scores, motor symptoms before surgery, female gender, gastrointestinal comorbidities, surgical procedure and surgeon’s experience with specific techniques, and high signal intensity of cord in T2 MRI were the surgical outcome predictors reported in the literature. Lower Quality of Life (QoL) score and neck prior to surgery were reported as predictors of the more improved outcome, but high cord signal intensity in T2 MRI was reported as an unfavorable outcome predictor.
Passive Dynamic Ankle Foot Orthoses Use in Civilian Patients with Arthritic Conditions of the Foot and Ankle
Authors: Feng Jing , Weiss Jason , Thompson Austin , Meeker James E. 
Published: March 2023

Background: Nonsurgical interventions such as bracing with ankle foot orthoses (AFOs) aim to assist, restore, and redirect weightbearing forces to address difficulty with mobilization. We identified a custom carbon fiber passive dynamic ankle foot orthosis (PDAFO) that was designed to meet the needs of military combat veterans. We sought to evaluate the off-loading properties of one model of PDAFO (ExoSym) in a civilian population. Methods: Civilian patients 18 years or older were prescribed a PDAFO by a single surgeon. Pedobarographic data were obtained using the Tekscan F-Scan system. With the insole, participants were instructed to walk at a self-selected pace along a 20 m walkway under 3 conditions: (1) insole placed in between the brace and foot (over); (2) insole placed between the brace and insole of the shoe (under); (3) without the brace, the insole was placed in between the foot and insole of the shoe in both limbs (without). For assessment, forefoot and heel areas were evaluated with respect to maximal force, force*time integral (FTI), maximal contact area, maximal contact pressure, pressure*time integral (PTI), center of force (COF) excursion. Results: Six patients with arthritic foot and ankle conditions completed pedobarographic assessment for analysis. The brace reduced forefoot maximal force and contact pressures by 66% and 49%, respectively (538 ± 236 to 185 ± 130 N [ P < .001], and 99 ± 38 to 50 ± 24 P < .002). Additionally, participants were observed to load the forefoot portion of the brace with double the maximum contact pressures compared to the unbraced foot (204 ± 57 to 99 ± 38 kPa, P < .001). Conclusion: The results of this study showed that the PDAFO unloaded substantial force and pressure experienced by the forefoot. Participants loaded the brace to a greater extent than when going unbraced. ADAFO can provide measurable pressure relief for patients with arthritic conditions. Level of Evidence: Level IV, case series.
The Probable Protective Effect of Photobiomodulation on the Immunologic Factor’s mRNA Expression Level in the Lung: An Extended COVID-19 Preclinical and Clinical Meta-analysis
Authors: Arjmand Babak , Rahim Fakher 
Published: March 2023

Background: Different expression of cytokine genes in the body determines the type of immune response (Th1 or Th2), which can play an important role in the pathogenesis of the COVID-19 disease. Aims: This meta-analysis was conducted to evaluate the probable effect of photobiomodulation (PBMT) therapy on the cytokine’s mRNA expression in the lung. Methods: We systematically searched indexing databases, including PubMed/Medline, ISI web of science, Scopus, EMBASE, and Cochrane central, using standard terms without language, study region or type restrictions. Studies on using PBM in lung injury modeling with samples collected from lung tissue to observe IL-1β, TNF-α, IL-10, and IL-6 mRNA expression were included. RevMan 5.3 software was used for data analysis and standardized mean difference as effect size. Results: Of the 438 studies found through initial searches, 17 met the inclusion criteria. The main properties of 13 articles on 384 animals included in this meta-analysis with a wide range of species include rats (n = 10) and rabbits (n = 3). The analysis revealed that PBMT reduced the mRNA expression of TNFα (SMD: −3.70, 95% CI: −6.29, −1.11, P = .005, I2 = 71%) and IL-1β (SMD: −5.85, 95% CI: −8.01, −3.69, P < .00001, I2 = 37%) significantly, but no statistically significant reduction in IL-6 (SMD: −2.89, 95% CI: −5.79, 0.01, P = .05, I2 = 88%) was observed compared with the model controls. Also, PBMT increased IL-10 mRNA expression significantly compared with the model controls (SMD: 1.04, 95% CI: 0.43, 1.64, P = .0008, I2 = 17%). Conclusion: This meta-analysis revealed that the PBMT utilizes beneficial anti-inflammatory effects and modulation of the immune system on lung damage in animal models and clinical studies. However, animal models and clinical studies appear limited considering the evidence’s quality; therefore, large clinical trials are still required.
Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network
Authors: Jin Yuhong , Hou Lei , Lu Zhenyong , Chen Yushu 
Published: March 2023

AbstractThe crack fault is one of the most common faults in the rotor system, and researchers have paid close attention to its fault diagnosis. However, most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals. In this paper, a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function (RBF) network and Pattern recognition neural network (PRNN) is presented. Firstly, a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method, where the crack’s periodic opening and closing pattern and different degrees of crack depth are considered. Then, the dynamic response is obtained by the harmonic balance method. By adjusting the crack parameters, the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots. The analysis results show that the first critical speed, first subcritical speed, first critical speed amplitude, and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis. Based on this, the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input. Test results show that the proposed method has high fault diagnosis accuracy. This research proposes a crack detection method adequate for the hollow shaft rotor system, where the crack depth and position are both unknown.
DADOS: A Cloud-based Data-driven Design Optimization System
Authors: Song Xueguan , Wang Shuo , Zhao Yonggang , Liu Yin , Li Kunpeng 
Published: March 2023

AbstractThis paper presents a cloud-based data-driven design optimization system, named DADOS, to help engineers and researchers improve a design or product easily and efficiently. DADOS has nearly 30 key algorithms, including the design of experiments, surrogate models, model validation and selection, prediction, optimization, and sensitivity analysis. Moreover, it also includes an exclusive ensemble surrogate modeling technique, the extended hybrid adaptive function, which can make use of the advantages of each surrogate and eliminate the effort of selecting the appropriate individual surrogate. To improve ease of use, DADOS provides a user-friendly graphical user interface and employed flow-based programming so that users can conduct design optimization just by dragging, dropping, and connecting algorithm blocks into a workflow instead of writing massive code. In addition, DADOS allows users to visualize the results to gain more insights into the design problems, allows multi-person collaborating on a project at the same time, and supports multi-disciplinary optimization. This paper also details the architecture and the user interface of DADOS. Two examples were employed to demonstrate how to use DADOS to conduct data-driven design optimization. Since DADOS is a cloud-based system, anyone can access DADOS atwww.dados.com.cnusing their web browser without the need for installation or powerful hardware.
Bayesian optimization of hydrogen plasma treatment in silicon quantum dot multilayer and application to solar cells
Authors: Kumagai Fuga , Gotoh Kazuhiro , Miyamoto Satoru , Kato Shinya , Kutsukake Kentaro , Usami Noritaka , Kurokawa Yasuyoshi 
Published: March 2023

AbstractSilicon quantum dot multilayer (Si-QDML) is a promising material for a light absorber of all silicon tandem solar cells due to tunable bandgap energy in a wide range depending on the silicon quantum dot (Si-QD) size, which is possible to overcome the Shockley–Queisser limit. Since solar cell performance is degenerated by carrier recombination through dangling bonds (DBs) in Si-QDML, hydrogen termination of DBs is crucial. Hydrogen plasma treatment (HPT) is one of the methods to introduce hydrogen into Si-QDML. However, HPT has a large number of process parameters. In this study, we employed Bayesian optimization (BO) for the efficient survey of HPT process parameters. Photosensitivity (PS) was adopted as the indicator to be maximized in BO. PS (σp/σd) was calculated as the ratio of photoconductivity (σp) and dark conductivity (σd) of Si-QDML, which allowed the evaluation of important electrical characteristics in solar cells easily without fabricating process-intensive devices. 40-period layers for Si-QDML were prepared by plasma-enhanced chemical vapor deposition method and post-annealing onto quartz substrates. Ten samples were prepared by HPT under random conditions as initial data for BO. By repeating calculations and experiments, the PS was successfully improved from 22.7 to 347.2 with a small number of experiments. In addition, Si-QD solar cells were fabricated with optimized HPT process parameters; open-circuit voltage (VOC) and fill factor (FF) values of 689 mV and 0.67, respectively, were achieved. These values are the highest for this type of device, which were achieved through an unprecedented attempt to combine HPT and BO. These results prove that BO is effective in accelerating the optimization of practical process parameters in a multidimensional parameter space, even for novel indicators such as PS.
Long read isoform sequencing reveals hidden transcriptional complexity between cattle subspecies
Authors: Ren Yan , Tseng Elizabeth , Smith Timothy P. L. , Hiendleder Stefan , Williams John L. , Low Wai Yee 
Published: March 2023

AbstractThe Iso-Seq method of full-length cDNA sequencing is suitable to quantify differentially expressed genes (DEGs), transcripts (DETs) and transcript usage (DTU). However, the higher cost of Iso-Seq relative to RNA-seq has limited the comparison of both methods. Transcript abundance estimated by RNA-seq and deep Iso-Seq data for fetal liver from two cattle subspecies were compared to evaluate concordance. Inter-sample correlation of gene- and transcript-level abundance was higher within technology than between technologies. Identification of DEGs between the cattle subspecies depended on sequencing method with only 44 genes identified by both that included 6 novel genes annotated by Iso-Seq. There was a pronounced difference between Iso-Seq and RNA-seq results at transcript-level wherein Iso-Seq revealed several magnitudes more transcript abundance and usage differences between subspecies. Factors influencing DEG identification included size selection during Iso-Seq library preparation, average transcript abundance, multi-mapping of RNA-seq reads to the reference genome, and overlapping coordinates of genes. Some DEGs called by RNA-seq alone appear to be sequence duplication artifacts. Among the 44 DEGs identified by both technologies some play a role in immune system, thyroid function and cell growth. Iso-Seq revealed hidden transcriptional complexity in DEGs, DETs and DTU genes between cattle subspecies previously missed by RNA-seq.
Safety and tolerability of Sacubitril/Valsartan in heart failure patient with reduced ejection fraction
Authors: Khan Muhammad Nauman , Soomro Najia Aslam , Naseeb Khalid , Bhatti Usman Hanif , Rauf Rubina , Balouch Iram Jehan , Moazzam Ali , Bashir Sonia , Ashraf Tariq , Karim Musa 
Published: March 2023

Abstract
Background
Angiotensin receptor blocker and a neprilysin inhibitor (ARNI) has emerged as an innovative therapy for patients of heart failure with reduced ejection fraction (HFrEF). The purpose of this study was to assess the safety and tolerability of Sacubitril/Valsartan in patient with HFrEF in Pakistani population.

Methods
This proof-of-concept, open label non-randomized clinical trial was conducted at a tertiary care cardiac center of Karachi, Pakistan. Patients with HFrEF were prescribed with Sacubitril/Valsartan and followed for 12 weeks for the assessment of safety and tolerability. Safety measures included incidence of hypotension, renal dysfunction, hyperkalemia, and angioedema.

Results
Among the 120 HFrEF patients, majority were male (79.2%) with means age of 52.73 ± 12.23 years. At the end of 12 weeks, four (3.3%) patients died and eight (6.7%) dropped out of the study. In the remaining 108 patients, 80.6% (87) of the patients were tolerant to the prescribed dose. Functional class improved gradually with 75.0% (81) in class I and 24.1% (26) in class II, and only one (0.9%) patient in class III at the end of 12 weeks. Hyperkalemia remains the main safety concern with incidence rate of 21.3% (23) followed by hypotension in 19.4% (21), and renal dysfunction in 3.7% (4) of the patients.

Conclusions
Sacubitril/Valsartan therapy in HFrEF patients is safe and moderately tolerated among the Pakistani population. It can be used as first line of treatment for these patients.

Trial registration

NCT05387967. Registered 24 May 2022—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT05387967
Data collection systems for active safety surveillance of vaccines during pregnancy in low- and middle-income countries: developing and piloting an assessment tool (VPASS)
Authors: Belizán Maria , Rodriguez Cairoli Federico , Mazzoni Agustina , Goucher Erin , Zaraa Sabra , Matthews Sarah , Pingray Verónica , Stergachis Andy , Xiong Xu , Berrueta Mabel , Buekens Pierre 
Published: March 2023

Abstract
Background
There is an urgent need for active safety surveillance to monitor vaccine exposure during pregnancy in low- and middle-income countries (LMICs). Existing maternal, newborn, and child health (MNCH) data collection systems could serve as platforms for post-marketing active surveillance of maternal immunization safety. To identify sites using existing systems, a thorough assessment should be conducted. Therefore, this study had the objectives to first develop an assessment tool and then to pilot this tool in sites using MNCH data collection systems through virtual informant interviews.

Methods
We conducted a rapid review of the literature to identify frameworks on population health or post-marketing drug surveillance. Four frameworks that met the eligibility criteria were identified and served to develop an assessment tool capable of evaluating sites that could support active monitoring of vaccine safety during pregnancy. We conducted semi-structured interviews in six geographical sites using MNCH data collection systems (DHIS2, INDEPTH, and GNMNHR) to pilot domains included in the assessment tool.

Results
We developed and piloted the “VPASS (Vaccines during Pregnancy – sites supporting Active Safety Surveillance) assessment tool” through interviews with nine stakeholders, including central-level systems key informants and site-level managers from DHIS2 and GNMNHR; DHIS2 in Kampala (Uganda) and Kigali (Rwanda); GNMNHR from Belagavi (India) and Lusaka (Zambia); and INDEPTH from Nanoro (Burkina Faso) and Manhica (Mozambique). The tool includes different domains such as the system’s purpose, the scale of implementation, data capture and confidentiality, type of data collected, the capability of integration with other platforms, data management policies and data quality monitoring. Similarities among sites were found regarding some domains, such as data confidentiality, data management policies, and data quality monitoring. Four of the six sites met some domains to be eligible as potential sites for active surveillance of vaccinations during pregnancy, such as a routine collection of MNCH individual data and the capability of electronically integrating individual MNCH outcomes with information related to vaccine exposure during pregnancy. Those sites were: Rwanda (DHIS2), Manhica (IN-DEPTH), Lusaka (GNMNHR), and Belagavi (GNMNHR).

Conclusion
This study's findings should inform the successful implementation of active safety surveillance of vaccines during pregnancy by identifying and using active individual MNCH data collection systems in LMICs.
Rural job preferences of graduate class medical students in Ethiopia—a discrete choice experiment (DCE)
Authors: Engidaw Mamo , Alemu Melaku Birhanu , Muche Getasew Amare , Yitayal Mezgebu 
Published: March 2023

Abstract
Background
Human resource is one of the health system’s building blocks, which ultimately leads to improved health status, equity, and efficiency. However, human resources in the health sector are characterized by high attrition, distributional imbalance, and geographic inequalities in urban and rural settings.

Methods
An discrete choice experiment (DCE) with 16 choice tasks with two blocks containing five attributes (salary, housing, drug and medical equipment, year of experience before study leave, management support, and workload) were conducted. A latent class and mixed logit model were fitted to estimate the rural job preferences and heterogeneity. Furthermore, the relative importance, willingness to accept and marginal choice probabilities were calculated. Finally, the interaction of preference with age and sex was tested.

Results
A total of 352 (5632 observations) final-year medical students completed the choice tasks. On average, respondents prefer to work with a higher salary with a superior housing allowance In addition, respondents prefer a health facility with a stock of drug and medical equipment which provide education opportunities after one year of service with supportive management with a normal workload. Young medical students prefer lower service years more than older students. Besides age and service year, we do not find an interaction between age/sex and rural job preference attributes.

A three-class latent class model best fits the data. The salary was the most important attribute in classes 1 and 3. Contrary to the other classes, respondents in class 2 do not have a significant preference for salary. Respondents were willing to accept an additional 4271 ETB (104.2 USD), 1998 ETB (48.7 USD), 1896 ETB (46.2 USD), 1869 (45.6 USD), and 1175 ETB (28.7 USD) per month for the inadequate drug and medical supply, mandatory two years of service, heavy workload, unsupportive management, and basic housing, respectively.

Conclusion
Rural job uptake by medical students was influenced by all the attributes, and there was individual and group-level heterogeneity in preference. Policymakers should account for the job preferences and heterogeneity to incentivize medical graduates to work in rural settings and minimize attrition.
An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems
Authors: Kwok Stephen Wai Hang , Wang Guanjin , Sohel Ferdous , Kashani Kianoush B. , Zhu Ye , Wang Zhen , Antpack Eduardo , Khandelwal Kanika , Pagali Sandeep R. , Nanda Sanjeev , Abdalrhim Ahmed D. , Sharma Umesh M. , Bhagra Sumit , Dugani Sagar , Takahashi Paul Y. , Murad Mohammad H. , Yousufuddin Mohammed 
Published: March 2023

Abstract
Background
We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores.

Methods
This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis.

Results
Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849–0.856, calibration slopes 0.911–1.173, and Hosmer–Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores.

Conclusion
The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.