bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and validation of a prognostic model based on SEER data for patients with Combined small-cell lung cancer
Authors: Qie Shuai , Shi Hongyun , Luan Jiusong , Yuan Lanhui , Peng Yu , Liu Fangyu 
Published: April 2023

Abstract
Background: To analyze the prognostic factors of combined small cell lung cancer (CSCLC) and construct a nomogram model for CSCLC.
Methods:
A total of 978 patients diagnosed with CSCLC from 2010 to 2015 were collected based on the SEER database. According to the ratio of 7:3, the patients were divided into the modeling group and the testing group. Univariate and multivariate Cox regression analyses were performed on the patients in the modeling group to analyze the independent factors affecting the prognosis of CSCLC patients and construct a nomogram prediction model, which was verified by the C-index and calibration curve in the training cohort and the validation cohort, respectively.
Results:
Univariate and multivariate Cox regression analysis showed that N stage, M stage, surgery, chemotherapy, radiotherapy, age, brain metastases, lung metastases, liver metastases, bone metastases, and tumor size were independent risk factors affecting the prognosis of CSCLC patients (P<0.05). A nomogram prediction model was constructed based on the above 10 risk factors through visual analysis, and the C-index was 0.753 (95%CI: 0.727~0.750). The calibration curves showed good agreement between the 1 -, 2 -, and 3-year predicted and actual survival rates of the prediction model constructed in this study. The AUC of the 1-, 2-, and 3-year prediction models was 0.813, 0.814, and 0.802, respectively. DCA showed that the nomogram model had more clinical application value in predicting survival prognosis than TNM staging. Finally, according to the total score of the nomogram survival prediction model, all the included cases were divided into low-risk, intermediate-risk, and high-risk strata and these three survival curves of each risk stratification showed significant survival differences (p< 0.05).
Conclusions:
The nomogram prediction model constructed in this study has higher accuracy and clinical application value than the traditional TNM staging. It can predict the 1 -, 2 - and 3-year OS of patients individually and provide a new tool for clinicians to evaluate the survival prognosis of CSCLC. The risk stratification system established by this model can identify high-risk patients more quickly, and make follow-up plans and subsequent treatment plans more targeted.
Clinical and biological factors predisposing to hyperkalemic renal tubular acidosis in solid organ transplant recipients : a retrospective monocentric study
Authors: Mohib Othmane , Vanderhulst Julien , Catalano Concetta , Roussoulières Ana , Knoop Christiane , Baudoux Thomas 
Published: April 2023

Abstract
Background The occurrence of hyperkalemic renal tubular acidosis in the post-transplantation period is likely underestimate, and its identification remains important to offer adequate medical management. Transplanted recipients frequently present clinical and biological characteristics which are associated in the literature with the occurrence of this complication. Post-transplant exposure to drugs that cause hyperkalemic renal tubular acidosis may therefore appear risky. We conducted a single-center retrospective comparative study in which we compared patients with hyperkalemic renal tubular acidosis and a control group to identify clinical and biological factors significantly associated with this complication.Methods The Fisher’s exact test was applied to test whether there is a significant association between hyperkalemic renal tubular acidosis and qualitative variable. The Mann-Whitney test was applied for the quantitative variables. We performed a multivariate logistic regression excluding some variables and then selected the variables using a stepwise procedure. The best logistic model according to the Akaike Information Criterion was the final model.Results Kidney and heart transplant recipients appear to be at greater risk of developing this complication than lung transplant recipients (p = 0.016). No other qualitative variable showed a significant association with the occurrence of hyperkalemic RTA. We also found a significant difference in kalemia (p < 0.01), chloremia (p < 0.01), and bicarbonatemia (p < 0.01).Neither the residual serum tacrolimus level (p = 0.19) nor the creatinine level of renal transplant patients (p = 0.17) was significantly associated with hyperkalemic renal tubular acidosis. In multivariate logistic regression, the analysis of the final model confirms the significant impact of kalemia (p < 0.01), chloremia (p < 0.01), and bicarbonatemia (p = 0.013) in the occurrence of this complication.Conclusion In our study, the type of transplanted organ, kalemia, chloremia, and bicarbonatemia seem to be significantly associated with the occurrence of hyperkalemic renal tubular acidosis. It is essential to identify this complication and to treat hyperkalemia first before proposing additional treatments. Larger studies are needed to more accurately define transplanted patients at risk for hyperkalemic renal tubular acidosis.
TABHATE: A Target-based Hate Speech Detection Dataset in Hindi
Authors: Sharma Deepawali , Singh Vivek Kumar , Gupta Vedika 
Published: April 2023

Abstract
Social media has over the years provided a medium for creation and dissemination of opinions and thoughts through online platforms. While it allows users to express their views, sentiments and emotions, some people try to use it to generate and share unpleasant and hateful content. Such content is now referred to as hate speech and it may target an individual, a group, a community, or a country. During the last few years, several techniques have been developed to automatically detect and identify hate speech, offensive and abusive content from social media platforms. However, majority of the studies focused on hate speech detection in English language texts. With social media getting higher penetration across different geographies, there is now a significant amount of content generated in various languages. Though there have been significant advancements in algorithmic approaches for the task, the non-availability of suitable dataset in other languages poses a problem in research advancement in them. Hindi is one such widely spoken language where such datasets are not available. This work attempts to bridge this research gap by presenting a curated and annotated dataset for target-based hate speech (TABHATE) in the Hindi language. The dataset comprises of 2,020 tweets and is annotated by three independent annotators. A multiclass labelling is used where each tweet is labelled as: (i) individual targeting, (ii) community targeting, and (iii) none. Inter annotator agreement is computed. The suitability of dataset is then further explored by applying some standard deep learning and transformer-based models for the task of hate speech detection. The experimental results obtained show that the dataset can be used for experimental work on hate speech detection of Hindi language texts.
Export stability, income growth, and North-South difference
Authors: Li Lin , Ding Yunuo , Wang Yijia , Fan Jiachen 
Published: April 2023

Abstract
This paper examines the effect of export stability on income inequality in north and south countries. We use products level data to measure export stability and apply several IV strategies that allows us to estimate the impact of export stability on income with much greater precision. Our main finding is that export stability has a positive effect on the income growth of developing countries but no effect on developed countries, implying that export stability leads to a significant reduction in income inequality between the north and south countries. Furthermore, we show that in developing countries, the effect of export stability operates through an increase in income expectation and inventory management efficiency; the improvement of business environment and product complexity enhances the effect of export stability; the increase of government size may decrease the effect of trade instability. Finally, we show third country effect doesn’t directly influence income growth.
Almost Finite-time Synchronization for Coronary Artery Systems via Adaptive Fractional-powered-integral-type Sliding Mode Control Method
Authors: YANG Chi-Hsin , YANG Ruizhao , Liang Chuanjian , LIAN Wenwu , CHEN Hong-Yi , WANG Kun-Chieh 
Published: April 2023

Abstract
From the cardiac medical viewpoint, the treatment for a vasospasm caused by the diseased coronary artery system (CAS) is to synchronize the dynamic behavior of a diseased CAS with that of a healthy CAS such that the physiological change of the CAS is improved. In this study, for the bio-mathematical model of CAS, the novel fractional-powered-integral-type finite-time-convergent sliding mode (FFSM), which is consisted of the fractional powered and the integral terms of the state variables, is introduced. The adaptive sliding mode control method based on the defined FFSM is proposed for achieving the almost finite-time synchronization between two CASs. Once the developed control is in action, the special type of stability is exhibited. That is, the two error states are stabilized in the sequel on the sliding surface, which induces the almost finite-time stability owing to the property of the model for CAS. The adaptive FFSM control scheme, which includes three online updated gains with the rules of adaption, can counter the nonlinear dynamical effects of the synchronized error system. Numerical experiments are executed to show the validation of the present scheme.
Evidence of Polariton-Version Quantum Cherenkov Radiations
Authors: Wang Lei , Zheng Jia-Xin , Cui Lin , Liu Xue-Qing , Luo Yang , Chen Qi-Dai , Wang Haiyu 
Published: April 2023

Abstract
Strong similarities for relativistic electrons stimulated from electron beams and electron clouds are merging the boundary of wave-particle duality of electrons, one of which being superposition is Cherenkov radiations. Recent quantum Cherenkov effects for non-point particles predict electron-spin flip transitions in bound- and free-electron systems like bulk dielectrics and graphene, which, however, haven’t been experimentally exemplified to date. Here, we report an analogous polariton-version quantum Cherenkov radiation, where double-cone emission processes of Cherenkov-type phonon polaritons (PhPs) and derivatives in ultrashort-pulse-stimulated ferroelectric crystals and graphene are originated from transient non-point electron wave packets, and material-specific low-frequency cutoffs are due to electron-spin flipping. Component analysis of double-cone polariton emissions is achieved by a unified quantum dynamics model, and initial polariton dynamics including abnormal backward cones in graphene are restored. The resulting quantum wave packets of PhPs are further correlated with femtosecond-laser-induced modifications inside ferroelectric lithium niobates, which realize a PhP threshold criterion for ultrasmooth femtosecond laser nanofabrication.
Occlusion Robust Sign Language Recognition System for Indian Sign Language Using CNN and Pose Features
Authors: DAS SOUMEN , Biswas Saroj kr. , Purkayastha Biswajit 
Published: April 2023

Abstract
The Sign Language Recognition System (SLRS) is a cutting-edge technology that aims to enhance communication accessibility for the deaf community in India by replacing the traditional approach of using human interpreters. However, the existing SLRS for Indian Sign Language (ISL) do not focus on some major problems including occlusion, similar hand gesture, multi viewing angle problem and inefficiency due to extracting features from a large sequence of frame that contains redundant and unnecessary information. Therefore, in this research paper an occlusion robust SLRS named Multi Featured Deep Network (MF-DNet) is proposed for recognizing ISL words. The suggested MF-DNet uses a histogram difference based keyframe selection technique to remove redundant frames. To resolve occlusion, similar hand gesture, and multi viewing angle problem the suggested MF-DNet incorporates pose features with Convolution Neural Network (CNN) features. For classification the proposed system uses Bi Directional Long Shor Term Memory (BiLSTM) network, which is compared with different classifier such as LSTM, ConvLSTM and stacked LSTM networks. The proposed SLRS achieved an average classification accuracy of 96.88% on the ISL dataset and 99.06% on the benchmark LSA64 dataset. The results obtained from the MF-DNet is compared with some of the existing SLRS where the proposed method outperformed the existing methods.
App-delivered cognitive-behavioral therapy for insomnia among patients with comorbid musculoskeletal complaints and insomnia referred to 4-week inpatient multimodal rehabilitation: protocol for a randomized clinical trial
Authors: Skarpsno Eivind Schjelderup , Simpson Melanie Rae , Seim Arnfinn , Hrozanova Maria , Bakøy Marthe Alida , Klevanger Nina Elisabeth , Aasdahl Lene 
Published: April 2023

Abstract
Background: Insomnia is prevalent among patients receiving treatment for long-term musculoskeletal complaints in inpatient rehabilitation settings. Cognitive-behavioral therapy for insomnia (CBT-I) is effective for improving sleep quality in patients with pain, but a lack of therapists often limits the capacity to use this therapy in rehabilitation programs. The aim of this randomized clinical trial (RCT) is to evaluate the effectiveness of app-delivered CBT-I adjunct to inpatient multimodal rehabilitation for individuals with comorbid musculoskeletal complaints and insomnia, compared with rehabilitation (usual care) only.
Methods. This RCT has two parallel arms: 1)inpatient multimodal rehabilitation and 2)app-delivered CBT-I adjunct to inpatient multimodal rehabilitation. Patients referred to Unicare Helsefort (Norway) with long-term chronic musculoskeletal complaints are invited to the study. Eligible and consenting participants will be randomized to the intervention and usual care at a ratio of 2:1. Outcomes are collected at baseline, at the end of rehabilitation (6 weeks), 3 months, 7.5 months, and 13.5 months of follow-up. The primary outcome is insomnia severity measured at 3 months. Secondary outcomes include pain intensity, health-related quality of life, sick leave, and prescribed medication. Exploratory analyses are planned to identify moderators and mediators of the effect of the app-delivered intervention. We will also conduct a process evaluation that will assess facilitators and barriers for treatment adherence and a qualitative study to explore how the participants experience the use of digital therapy.
Discussion:This RCT will provide novel knowledge about the effectiveness of app-delivered CBT-I as an adjunct to usual care among patients participating in inpatient multimodal pain rehabilitation. Regardless of the results from this trial, the results will improve our understanding of the utility of dCBT-I in the field of rehabilitation and the importance of adding sleep therapy to this patient group.
Trial registration: This trial was prospectively registered in ClinicalTrials.gov October 10, 2022 (ClinicalTrials.gov identifier: NCT05572697).
Study on protein hydrolysate from Symplectoteuthis oualaniensis reconstructing fibroblast against oxidative stress by hydrogen peroxide
Authors: Li Na , Diao Xiaozhen , Pu Xinyi , Tang Pengjie , Elango Jeevithan , Wu Wenhui 
Published: April 2023

AbstractThe free radical hypothesis has been accepted in the researching of skin aging. As a small molecule oxidant, hydrogen peroxide (H2O2) is easily induce the apoptosis of HSF cells through biofilm system. Oxidative stress caused by H2O2also leads to the synthesis of inflammatory cytokines, mainly including interleukin-1 (IL-1), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α), which are involved in the regulation of skin inflammation and irritation. Protein protease hydrolysates were prepared from Iris cuttlefish (Symplectoteuthis oualaniensis) and then investigated for their antioxidant activitiesin vitro. The positive effect by PHCSO (protease hydrolysates from the carcass ofSymplectoteuthis oualaniensis) on the reconstruction of HSF (human skin fibroblasts) cells against oxidative stress was investigated by the oxidative stress model via H2O2inducement. PHCSO-1 or PHCSO-2 was a mixed peptide whose molecular weight is over 10 kDa or less 10 kDa from the ultrafiltration of PHCSO. The reconstructing effect of PHCSO-2 was superior to that of PHCSO on HSF cells according to the results from cell morphology of fluorescence staining, inflammatory factors and antioxidant activity analysis. 5 mg/mL PHCSO-2 showed the DPPH and hydroxyl radical scavenging activity as 57.96% and 56.86% respectively, with TNF-α reduced by 24.83%, which indicated the effective protection on skin from oxidative stress. The protein hydrolysate fromSymplectoteuthis oualaniensiswas proved to be the marine antioxidant peptide, which could be applied in cosmetics, pharmaceutical or food industries.
Simple and secure thrombectomy involving peripheral pulmonary arteries for acute pulmonary embolism
Authors: Sasaki Hanae , Kowatari Ryosuke , Kondo Norihiro , Minakawa Masahito 
Published: April 2023

Abstract
Background:
Surgical pulmonary artery thrombectomy is a well-established emergency treatment for massive pulmonary embolism (PE) in which fibrinolysis or thrombolysis are not effective. However, surgery for massive PE that requires peripheral pulmonary artery thrombus removal remains challenging. We established a simple and secure pulmonary artery thrombectomy method using cardiopulmonary bypass and cardiac arrest. In this procedure, the surgical assistant arm, typically used for coronary artery bypass grafting, is used to obtain a feasible working space during thrombectomy.
Case presentation:
We present seven consecutive massive PE cases that were treated with the present surgical method and successfully weaned from cardiopulmonary bypass or extracorporeal membrane oxygenation postoperatively.
Conclusions:
This procedure can be used to prevent right ventricular failure after surgery as surgeons can remove the peripheral thrombus with clear vision up to the second branch of the pulmonary artery.
The relationship between antisocial personality and drug craving in Chinese male methamphetamine-dependent patients: the mediating role of alexithymia
Authors: Li Yanli , Wang Dongmei , Zhou Huixia , Tian Yang , Chen Jiajing , Zhu Rongrong , Li Yuqing , Fan Fusheng , Fu Fabing , Wei Dejun , Tang Shanshan , Xiangyang Zhang 
Published: April 2023

Abstract
Background In patients with methamphetamine use disorder (MUD), antisocial personality disorder (ASPD) and alexithymia increase the risk of drug craving, but the relationship between the three of them is unclear. Therefore, this study explored the mediating role of alexithymia in the relationship between ASPD and drug craving.Methods We recruited 524 MUD patients at a drug rehabilitation center in Sichuan Province, China, and assessed ASPD with the Mini International Neuropsychiatric Interview (M.I.N.I.), methamphetamine craving with the Desire for Drugs Questionnaire (DDQ), and alexithymia with the Toronto Affective Disorder Scale (TAS-20).Results Compared with MUD patients without ASPD, MUD patients with ASPD had higher DDQ-desire and intention, DDQ-negative reinforcement and DDQ-total scores, as well as TAS-total and their subscale scores (all p < 0.05). Correlation analyses revealed a significant positive correlation between ASPD, alexithymia and drug craving. Mediating effect analysis further indicated that the relationship between ASPD and drug craving was mediated by alexithymia.Conclusions Our study demonstrates for the first time that alexithymia mediates the relationship between ASPD and drug craving, which may provide a new entry point for treating MUD with comorbid ASPD.
Depressive symptomatology and NT-proBNP and Health status in Heart Failure
Authors: Balata Mahmoud , Westenfeld Ralf , Hassan Marwa , Pfister Roman , Zimmer Sebastian , Nickenig Georg , Becher Marc Ulrich , Conrad Rupert 
Published: April 2023

Abstract
Background: Although depression symptoms are common among heart failure (HF) patients, there is little consensus regarding the relationship between depressive symptoms and NT-proBNP, the key HF marker. Therefore, this study aimed to investigate this relationship and assess the impact of depressive symptoms on the health status and clinical outcomes of HF patients.
Methods: 151 patients with HF were enrolled in the study and followed up for one year. Depressive symptoms and health status were assessed by the Hospital Anxiety and Depression Scale (HADS-D) and the Kansas City Cardiomyopathy Questionnaire (KCCQ), respectively.
Results: At baseline, the median HADS-D score was 5 (3 - 8) points. Patients with HADS-D scores > 5 points had significantly higher NT-proBNP levels (p = 0.043), and significantly lower KCCQ-OSS and KCCQ-CSS (p < 0.001 for both KCCQ scores).
From baseline to the last assessment after one year the percentage changes in the HADS-D scores were significantly positively correlated with the percentage changes in the levels of NT-proBNP (r = 0.22, p = 0.009). It was also significantly negatively correlated with the changes in KCCQ-OSS (r = -0.34, p < 0.001) and KCCQ-CSS (r = -0.19, p = 0.021).
Moreover, baseline HADS-D score was an independent predictor of the percentage changes in NT-proBNP from baseline to the last assessment (ß = 3.91, p = 0.03), even after adjustment for age, sex, baseline LVEF and creatinine (ß = 3.99, p = 0.02).
In a Cox regression analysis baseline HADS-D score had a significant impact on the cardiovascular mortality, which remained consistent even after adjustment for age, sex, and baseline LVEF, NT-proBNP and creatinine (hazard ratio = 1.18, 95% CI = 1.0 – 1.4, p = 0.048).
Conclusions:
1. There is a significant correlation between depressive symptoms, NT-proBNP, and health status in HF patients.
2. Baseline HADS-D score has a significant impact on the cardiovascular mortality in HF patients.
Low Frequency DD-DWT based illumination compensated and color intensified features for Face Recognition
Authors: Chaudhary Juhi , Yadav Jyotsna 
Published: April 2023

Abstract
The efficacy of face recognition systems is significantly affected by uneven light deviations, incident over images from different directions. This work presents a light invariable color face recognition method that efficiently normalizes illumination variances along with substantial improvement in color intensities. The proposed method includes following steps: Initially, segregation of facial images into primary spectral color components is performed to effectively balance contrast and evenly amplify color intensity levels. The varying illumination effects are due to low frequency component of image; therefore, Difference of Gaussian (DoG) high-pass filter based homomorphic filtering is further utilized in HSI (Hue, Saturation and Intensity) space. This conversion into HSI color space is mainly performed to ward off any colors distortion that may prevail in images due to direct utilization of further improvements on each color channel. Thus, this model aids in easily separating the chromaticity component from intensity part of an image. The normalized intensity component is concatenated with unaltered chromaticity components to effectively possess color facial information. Subsequently, extraction of double density discrete wavelet transform (DD-DWT) based coefficients of images is achieved for selection of substantial discriminative frequency components of face images. The large facial feature vector space is hereafter projected over eigen subspace to reduce dimensions effectively. In this work, k-nearest neighbor linear classifier has been utilized to classify selective features. The effectiveness of investigated method has been assessed on AR and CMU-PIE color face databases and attained results are thus contrasted with prior techniques which benefit in establishing its superiority.