bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of liver segmental dose threshold for hepatocyte regeneration following liver stereotactic body radiation therapy
Authors: George Karishma , Chopra Supriya , Rajamanickam Karthick , Joshi Kishore , Swamidas Jamema , Shetty Nitin , Engineer Reena
Published: April 2024

Background & objectives:

There is limited evidence studying the relationship of liver segmental dose and segmental volume changes. The segmental dose thresholds could potentially allow for segmental regeneration after liver stereotactic body radiation therapy (SBRT). Given improved survival in hepatocellular cancer (HCC) and liver metastases and more salvage therapy options, this has become an important clinical question to explore. This study assesses the impact of liver segmental dose on segmental volume changes (gain or loss) after SBRT.

Methods:

Liver segmental contours were delineated on baseline and serial follow up triphasic computed tomography scans. The volumes of total liver and doses to total liver, uninvolved liver and individual segments were noted. A correlation was evaluated between liver/segmental volume and dose using Pearson’s correlation. Furthermore, receiver operator’s curve (ROC) analysis was performed to find the segmental dose, i.e. predictive for liver volume loss.

Results:

A total of 140 non-tumour liver segments were available for analysis in 21 participants. Overall, 13 participants showed loss of overall liver volume and eight showed gain of overall liver volume. The median dose in segments reporting an increase in volume was 9.1 Gy (7-36 Gy). The median dose in segments losing volume was 15.5 Gy (1-49 Gy). On ROC analysis, segmental dose >11 Gy was associated with volume loss. On univariate analysis, only liver segmental dose contributed to a significant segmental volume loss.

Interpretation & conclusions:

We propose from the findings of this study that in SBRT for large hepatocellular cancer or liver metastases, liver segments should be individually delineated. Furthermore, 3-5 liver segments may be preferentially subjected to <9 Gy to facilitate hepatocyte regeneration. Preferential sparing of uninvolved liver segments may improve outcomes in liver stereotaxyas lower segmental doses were associated with liver regeneration. This may have implications on future liver SBRT planning where segmental doses may be as important as the mean dose.
Outcome of individuals with alcoholic cirrhosis hospitalized with first decompensation and their predictors
Authors: Giri Suprabhat , Ingawale Sushrut , Harindanath Sidharth , Jain Mohit , Garg Pranav , Darak Harish , Kumar Sanjay , Kale Aditya , Shukla Akash
Published: April 2024

Background & objectives:

Alcohol is one of most common aetiologies of cirrhosis and decompensated cirrhosis is linked to higher morbidity and death rates. This study looked at the outcomes and mortality associated risk variables of individuals with alcoholic cirrhosis who had hospitalization with their first episode of decompensation.

Methods:

Individuals with alcoholic cirrhosis who were hospitalized with the first episode of decompensation [acute decompensation (AD) or acute-on-chronic liver failure (ACLF)] were included in the study and were prospectively followed up until death or 90 days, whichever was earlier.

Results:

Of the 227 study participants analyzed, 167 (73.56%) and 60 (26.43%) participants presented as AD and ACLF, respectively. In the ACLF group, the mortality rate at 90 days was higher than in the AD group (48.3 vs 32.3%, P=0.02). In the AD group, participants who initially presented with ascites as opposed to variceal haemorrhage had a greater mortality rate at 90 days (36.4 vs 17.1%, P=0.041). The chronic liver failure-consortium AD score and the lactate-free Asian Pacific Association for the study of the Liver-ACLF research consortium score best-predicted mortality in individuals with AD and ACLF.

Interpretation & Conclusions:

There is significant heterogeneity in the type of decompensation in individuals with alcoholic cirrhosis. We observed significantly high mortality rate among alcoholic participants hospitalized with initial decompensation; deaths occurring in more than one-third of study participants within 90 days.
HIV, hepatitis B & C in people who inject drugs in India: A systematic review of regional heterogeneity & overview of opioid substitution treatment
Authors: Shukla Lekhansh , Shivaprakash Prakrithi , Kumar M. Suresh
Published: April 2024

Background & objectives:

This systematic review evaluates the human immunodeficiency virus (HIV), hepatitis C virus (HCV) and hepatitis B virus (HBV) burden among people who inject drugs (PWIDs) in India. In addition, we selectively examined research on opioid substitution treatment (OST)-related services due to their role in antiviral treatment uptake and adherence.

Methods:

Data were sourced from peer-reviewed and government publications between 1991 and September 20, 2023, searched in MEDLINE, Scopus and EBSCOhost. English language studies reporting weighted prevalence or raw numbers and recruitment sites were included for review. Quality was assessed using the Joanna Briggs Institute tool. Data synthesis was done in graphs and tables.

Results:

We included 50 reports, yielding 150 HIV, 68 HCV and 24 HBV prevalence estimates across India, revealing significant regional heterogeneity. Notably, 16 States had a single community-based HIV estimate, and 19 States had limited or no HCV data. The highest HIV and HCV prevalence was in Manipur (74.7% and 97.5%, respectively) in 1996. Recent spikes included 50.2 per cent HIV prevalence in Punjab (2010) and 73 per cent HCV in Uttar Pradesh (2021). Nationally, OST coverage in 2020 was under five per cent, with some northeast, north and central States exceeding this, but most others were falling below two per cent. No studies on the cost-effectiveness of directly observed treatment models for OST were identified.

Interpretation & conclusions:

There is a lack of sufficiently granular and generalizable estimates for HIV prevalence and any estimates for HCV and HBV among PWIDs in large parts of the country. Community-based representative studies are required to quantify the prevalence and severity of these diseases and allocate resources.
Comparative assessment of implementation of Cigarette and Other Tobacco Products Act in three States of India: A pre-post study
Authors: Goel Sonu , Anjali C. , Kar Sitanshu Sekhar , Joseph Neema , Kumar Rajeev , Medini Shravan Kumar , Das Munmee
Published: April 2024

Background & objectives:

The Cigarette and Other Tobacco Products Act (COTPA) limits the sale of tobacco products, promotion and advertising and packaging of tobacco products in India. Periodic monitoring of the level of compliance with all sections of COTPA is obligatory to assess the effectiveness of the act.

Methods:

This pre-post study aimed to assess the change in the level of compliance with different sections of COTPA in selected States. A total of 3849 and 2176 samples were assessed during the study in November 2020 and October 2021, respectively; using the EpiData Software.

Results:

Average compliance with section 4 of COTPA was highest in Puducherry during baseline [51.88%, prevalence ratio (PR)=0.72, 95% confidence interval (CI): 0.51-1.02] and in Telangana during endline assessment (66.1%, PR=0.6, 95% CI: 0.48-0.74). A visible change with section 5 of COTPA was observed in Telangana (40.08 to 93.12%, PR=0.04, 95% CI: 0.02-0.07). Compliance with section 6a of COTPA decreased in Puducherry (81.34 to 71.83%, PR=1.84, 95% CI: 1.14-2.95) and increased for Meghalaya and Telangana. The average compliance with sections 7, 8 and 9 (for Puducherry 51.09 vs. 48.8%, Meghalaya 54.94 vs. 46.18% and Telangana 73 vs. 51.91%, respectively) decreased in all States. An increase in average compliance was observed with sections 4 and 5 (P=0.19 and 0.11, respectively) and a decrease with sections 7, 8 and 9 (P=0.02) of COTPA in all three States. For sections 6a (P=0.06) and 6b (P=0.01), a mixed response was noted.

Interpretation & conclusions:

The findings of this study suggest that the level of compliance with sections 4 and 5 of COTPA increased because they are strictly monitored compared to sections 7-9, which are less enforced by the States. There is a need to monitor sections 7-9 (pack warning) to fulfill the objectives related to the World Health Organization (WHO) Framework Convention on Tobacco Control.
Clinical effectiveness of ferric carboxymaltose (iv) versus iron sucrose (iv) in treatment of iron deficiency anaemia in pregnancy: A systematic review and meta-analysis
Authors: Srimathi G. , Revathy R. , Bagepally Bhavani Shankara , Joshi Beena
Published: April 2024

Background & objectives:

Iron deficiency anaemia (IDA) during pregnancy is treated with oral and parenteral iron. The objective of this review was to compare the clinical effectiveness, safety, pregnancy and neonatal outcomes of intravenous (iv) ferric carboxymaltose (FCM) and iv iron sucrose (IS) in treating IDA in pregnancy.

Methods:

The Department of Health Research funded this study. PubMed, Cochrane Library, EMBASE and Scopus were searched to include studies published till November 2022. The protocol was registered in PROSPERO (CRD42022306092). Pregnant women (15-49 yr) in second and third trimesters, diagnosed with moderate-to-severe iron deficiency anaemia, treated with either of the drugs were included. The included studies were critically assessed using appropriate tools. We conducted a qualitative synthesis of the studies and meta-analysis for improvement in haematological parameters and incidence of adverse events.

Results:

A total of 18 studies were included. The risk of bias was low to moderate. A rise in haemoglobin up to four weeks was higher with FCM than IS by 0.57 (0.24, 0.9) g/dl. Intravenous FCM is associated with fewer adverse events than IS [pooled odds ratio: 0.5 (0.32, 0.79)]. The included studies had limited evidence on pregnancy and neonatal outcomes after iv iron treatment.

Interpretation & conclusions:

Intravenous FCM is effective and safer than intravenous IS in terms of haematological parameters, in treating IDA in pregnancy. Further research is required on the effects of iv FCM and iv IS on the pregnancy and neonatal outcomes when used for treating IDA in pregnancy.
Clinico-epidemiological profiles & outcome of severe malaria in children under-five in the tribal area of Kalahandi, Odisha
Authors: Edassery Aquinas , Meher Ajay Kumar , Gupta Vanshika , Rodriguez Rashmi
Published: April 2024

Background & objectives:

Severe malaria is a cause of excess mortality and morbidity in children in malaria-endemic areas where indigenous people live. Currently, available reports are all from secondary or tertiary care hospitals across India and some African countries. The objective of this study was to assess the clinical and epidemiological profiles of children under five years in two primary care health centres located in tribal-dominant Thuamul Rampur Block of Kalahandi district, Odisha. The outcome of management of severe malaria in these children was also assessed.

Methods:

A retrospective review of case records of children under five years of age diagnosed and admitted with severe malaria in two non-governmental primary care facilities between 2017 and 2022, was undertaken.

Results:

There was a declining trend in malaria cases documented in primary care health facilities between January 2017 and June 2022. Of the 4858 cases recorded, 242 (4.9%) had severe malaria, of whom 70.7 per cent (n=171) were children under 5 yr. The median age of the study children was 24 months (16-36). Children aged 1-2 yr had a significantly higher risk of malaria. The majority were tribals (87%), more than half the children presented with neurological manifestations (64.4%), and 49.6 per cent had respiratory manifestations, while 20.5 per cent had severe anaemia (Hb <5 g/dl). Most, 167 (97.7%) severe malaria was due to Plasmodium falciparum. Thirty-two percent of children were severely wasted (WHZ < -3 SD) and 28 per cent were moderately wasted (WHZ <-2 SD). There was no fatality among the 171 children who were managed for severe malaria in the two primary care facilities.

Interpretation & conclusions:

In high endemic areas severe malaria is predominantly a disease of under-five children and is caused by P. falciparum. Clinical manifestations of severe malaria in children can be varied and life-threatening. Primary health facilities can manage severe malaria successfully, thereby reducing child mortality. Effective collaboration between malaria control and nutrition intervention programmes is essential for appropriate case management.
Molecular detection of Orientia tsutsugamushi in ectoparasites & their small mammal hosts captured from scrub typhus endemic areas in Madurai district, India
Authors: Govindarajan R. , Sankar S. Gowri , Kumar M. Senthil , Rajamannar V. , Krishnamoorthi R. , Anand A. Alwin Prem , Kumar Ashwani , Samuel P. Philip
Published: April 2024

Background & objectives:

Scrub typhus, caused by Orientia tsutsugamushi present in small mammals harbouring the ectoparasites. A study was undertaken to detect the pathogen present in small mammals and its ectoparasites in the scrub typhus-reported areas.

Methods:

The small mammals (rodents/shrews) and its ectoparasites were screened for O. tsutsugamushi using nested PCR amplification of the groEL gene. Small mammals were collected by trapping and screened for ectoparasites (mites, ticks and fleas) by combing method.

Results:

All the chigger mites collected were tested negative for O. tsutsugamushi. Interestingly, adult non-trombiculid mites (Oribatida sp., Dermanyssus gallinae), fleas (Xenopsylla astia, X. cheopis, Ctenophalides felis and Ctenophalides sp.) and ticks (Rhipicephalus sanguineus, R. haemaphysaloides) screened were found to be positive for O. tsutsugamushi, which the authors believe is the first report on these species globally. Bandicota bengalensis with O. tsutsugamushi infection is reported for the first time in India. The O. tsutsugamushi groEL sequences from the positive samples were similar to the reference strains, Karp and Ikeda and phylogenetically clustered in clade IV with less evolutionary divergence. The blood samples of Rattus rattus, Suncus murinus and B. bengalensis collected from this area were tested positive for O. tsutsugamushi; interestingly, the sequence similarity was much pronounced with their ectoparasites indicating the transmission of the pathogen to host or vice versa.

Interpretation & conclusions:

The outcome of the present investigations widened our scope on the pathogens present in ectoparasites and rodents/shrews from this area. This will help to formulate the required vector control methods to combat zoonotic diseases.
Neutralizing antibody responses to SARS-CoV-2 Omicron variants: Post six months following two-dose & three-dose vaccination of ChAdOx1 nCoV-19 or BBV152
Authors: Yadav Pragya D. , Sardana Viren , Deshpande Gururaj Rao , Shinde Pradnya V. , Thangaraj Jeromie Wesley Vivian , George Leyanna S. , Sapkal Gajanan N. , Patil Deepak Y. , Sahay Rima R. , Shete Anita M. , Joshi Madhavi , Murhekar Manoj , Godbole Sheela , Gupta Nivedita , Prakash Satyartha , Rathore Mamta , Ujjainiya Rajat , Singh Ajay Pratap , Mishra Aastha , Dash Debasis , Chaudhary Kumardeep , Sengupta Shantanu ,   ICMR Booster Dose Study Group
Published: April 2024

Background & objectives:

The Omicron sub-lineages are known to have higher infectivity, immune escape and lower virulence. During December 2022 - January 2023 and March - April 2023, India witnessed increased SARS-CoV-2 infections, mostly due to newer Omicron sub-lineages. With this unprecedented rise in cases, we assessed the neutralization potential of individuals vaccinated with ChAdOx1 nCoV (Covishield) and BBV152 (Covaxin) against emerging Omicron sub-lineages.

Methods:

Neutralizing antibody responses were measured in the sera collected from individuals six months post-two doses (n=88) of Covishield (n=44) or Covaxin (n=44) and post-three doses (n=102) of Covishield (n=46) or Covaxin (n=56) booster dose against prototype B.1 strain, lineages of Omicron; XBB.1, BQ.1, BA.5.2 and BF.7.

Results:

The sera of individuals collected six months after the two-dose and the three-dose demonstrated neutralizing activity against all variants. The neutralizing antibody (NAbs) level was highest against the prototype B.1 strain, followed by BA5.2 (5–6 fold lower), BF.7 (11–12 fold lower), BQ.1 (12 fold lower) and XBB.1 (18–22 fold lower).

Interpretation & conclusions:

Persistence of NAb responses was comparable in individuals with two- and three-dose groups post six months of vaccination. Among the Omicron sub-variants, XBB.1 showed marked neutralization escape, thus pointing towards an eventual immune escape, which may cause more infections. Further, the correlation of study data with complete clinical profile of the participants along with observations for cell-mediated immunity may provide a clear picture for the sustained protection due to three-dose vaccination as well as hybrid immunity against the newer variants.
Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning
Authors: Mishra Priya , Boopal Balaji , Mishra Naveen
Published: April 2024

In the realm of Unmanned Aerial Vehicles (UAVs) for civilian applications, the surge in demand has underscored the need for sophisticated technologies. The integration of Unmanned Aerial Systems (UAS) with Artificial Intelligence (AI) has become paramount to address challenges in urban environments, particularly those involving obstacle collision risks. These UAVs are equipped with advanced sensor arrays, incorporating LiDAR and computer vision technologies. The AI algorithm undergoes comprehensive training on an embedded machine, fostering the development of a robust spatial perception model. This model enables the UAV to interpret and navigate through the intricate urban landscape with a human-like understanding of its surroundings. During mission execution, the AI-driven perception system detects and localizes objects, ensuring real-time awareness. This study proposes an innovative real-time three-dimensional (3D) path planner designed to optimize UAV trajectories through obstacle-laden environments. The path planner leverages a heuristic A* algorithm, a widely recognized search algorithm in artificial intelligence. A distinguishing feature of this proposed path planner is its ability to operate without the need to store frontier nodes in memory, diverging from conventional A* implementations. Instead, it relies on relative object positions obtained from the perception system, employing advanced techniques in simultaneous localization and mapping (SLAM). This approach ensures the generation of collision-free paths, enhancing the UAV's navigational efficiency. Moreover, the proposed path planner undergoes rigorous validation through Software-In-The-Loop (SITL) simulations in constrained environments, leveraging high-fidelity UAV dynamics models. Preliminary real flight tests are conducted to assess the real-world applicability of the system, considering factors such as wind disturbances and dynamic obstacles. The results showcase the path planner's effectiveness in providing swift and accurate guidance, thereby establishing its viability for real-time UAV missions in complex urban scenarios.
Sharp regularization effect for the non-cutoff Boltzmann equation with hard potentials
Authors: Chen Jun-Ling , Li Wei-Xi , Xu Chao-Jiang
Published: April 2024

For the Maxwellian molecules or hard potentials case, we verify the smoothing effect for the spatially inhomogeneous Boltzmann equation without angular cutoff. Given initial data with low regularity, we prove solutions at any positive time are analytic for strong angular singularity, and in the Gevrey class with optimal index for mild angular singularity. To overcome the degeneracy in the spatial variable, a family of well-chosen vector fields with time-dependent coefficients will play a crucial role, and the sharp regularization effect of weak solutions relies on a quantitative estimate on directional derivatives in these vector fields.
Pre-operative enhanced magnetic resonance imaging combined with clinical features predict early recurrence of hepatocellular carcinoma after radical resection
Authors: Chen Jian-Ping , Yang Ri-Hui , Zhang Tian-Hui , Liao Li-An , Guan Yu-Ting , Dai Hai-Yang
Published: April 2024

BACKGROUND

Indentifying predictive factors for postoperative recurrence of hepatocellular carcinoma (HCC) has great significance for patient prognosis.

AIM

To explore the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) combined with clinical features in predicting early recurrence of HCC after resection.

METHODS

A total of 161 patients with pathologically confirmed HCC were enrolled. The patients were divided into early recurrence and non-early recurrence group based on the follow-up results. The clinical, laboratory, pathological results and Gd-EOB-DTPA enhanced MRI imaging features were analyzed.

RESULTS

Of 161 patients, 73 had early recurrence and 88 were had non-early recurrence. Univariate analysis showed that patient age, gender, serum alpha-fetoprotein level, the Barcelona Clinic Liver Cancer stage, China liver cancer (CNLC) stage, microvascular invasion (MVI), pathological satellite focus, tumor size, tumor number, tumor boundary, tumor capsule, intratumoral necrosis, portal vein tumor thrombus, large vessel invasion, nonperipheral washout, peritumoral enhancement, hepatobiliary phase (HBP)/tumor signal intensity (SI)/peritumoral SI, HBP peritumoral low signal and peritumoral delay enhancement were significantly associated with early recurrence of HCC after operation. Multivariate logistic regression analysis showed that patient age, MVI, CNLC stage, tumor boundary and large vessel invasion were independent predictive factors. External data validation indicated that the area under the curve of the combined predictors was 0.861, suggesting that multivariate logistic regression was a reasonable predictive model for early recurrence of HCC.

CONCLUSION

Gd-EOB-DTPA enhanced MRI combined with clinical features would help predicting the early recurrence of HCC after operation.
Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique
Authors: Hu Wen-Jing , Bai Gang , Wang Yan , Hong Dong-Mei , Jiang Jin-Hua , Li Jia-Xun , Hua Yin , Wang Xin-Yu , Chen Ying
Published: April 2024

BACKGROUND

Postoperative delirium, particularly prevalent in elderly patients after abdominal cancer surgery, presents significant challenges in clinical management.

AIM

To develop a synthetic minority oversampling technique (SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.

METHODS

In this retrospective cohort study, we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022. The incidence of postoperative delirium was recorded for 7 d post-surgery. Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not. A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium. The SMOTE technique was applied to enhance the model by oversampling the delirium cases. The model’s predictive accuracy was then validated.

RESULTS

In our study involving 611 elderly patients with abdominal malignant tumors, multivariate logistic regression analysis identified significant risk factors for postoperative delirium. These included the Charlson comorbidity index, American Society of Anesthesiologists classification, history of cerebrovascular disease, surgical duration, perioperative blood transfusion, and postoperative pain score. The incidence rate of postoperative delirium in our study was 22.91%. The original predictive model (P1) exhibited an area under the receiver operating characteristic curve of 0.862. In comparison, the SMOTE-based logistic early warning model (P2), which utilized the SMOTE oversampling algorithm, showed a slightly lower but comparable area under the curve of 0.856, suggesting no significant difference in performance between the two predictive approaches.

CONCLUSION

This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods, effectively addressing data imbalance.
Prognostic analysis of related factors of adverse reactions to immunotherapy in advanced gastric cancer and establishment of a nomogram model
Authors: He Xu-Xu , Du Bang , Wu Tao , Shen Hao
Published: April 2024

BACKGROUND

Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years. However, the adverse reactions of immunotherapy and its relationship with patient prognosis still need further study. In order to determine the association between adverse reaction factors and prognosis, the aim of this study was to conduct a systematic prognostic analysis. By comprehensively evaluating the clinical data of patients with advanced gastric cancer treated by immunotherapy, a nomogram model will be established to predict the survival status of patients more accurately.

AIM

To explore the characteristics and predictors of immune-related adverse reactions (irAEs) in advanced gastric cancer patients receiving immunotherapy with programmed death protein-1 (PD-1) inhibitors and to analyze the correlation between irAEs and patient prognosis.

METHODS

A total of 140 patients with advanced gastric cancer who were treated with PD-1 inhibitors in our hospital from June 2021 to October 2023 were selected. Patients were divided into the irAEs group and the non-irAEs group according to whether or not irAEs occurred. Clinical features, manifestations, and prognosis of irAEs in the two groups were collected and analyzed. A multivariate logistic regression model was used to analyze the related factors affecting the occurrence of irAEs, and the prediction model of irAEs was established. The receiver operating characteristic (ROC) curve was used to evaluate the ability of different indicators to predict irAEs. A Kaplan-Meier survival curve was used to analyze the correlation between irAEs and prognosis. The Cox proportional risk model was used to analyze the related factors affecting the prognosis of patients.

RESULTS

A total of 132 patients were followed up, of whom 63 (47.7%) developed irAEs. We looked at the two groups’ clinical features and found that the two groups were statistically different in age ≥ 65 years, Ki-67 index, white blood cell count, neutrophil count, and regulatory T cell (Treg) count (all P < 0.05). Multivariate logistic regression analysis showed that Treg count was a protective factor affecting irAEs occurrence (P = 0.030). The ROC curve indicated that Treg + Ki-67 + age (≥ 65 years) combined could predict irAEs well (area under the curve = 0.753, 95% confidence interval: 0.623-0.848, P = 0.001). Results of the Kaplan-Meier survival curve showed that progression-free survival (PFS) was longer in the irAEs group than in the non-irAEs group (P = 0.001). Cox proportional hazard regression analysis suggested that the occurrence of irAEs was an independent factor for PFS (P = 0.006).

CONCLUSION

The number of Treg cells is a separate factor that affects irAEs in advanced gastric cancer patients receiving PD-1 inhibitor immunotherapy. irAEs can affect the patients’ PFS and result in longer PFS. Treg + Ki-67 + age (≥ 65 years old) combined can better predict the occurrence of adverse reactions.
Is recovery enhancement after gastric cancer surgery really a safe approach for elderly patients?
Authors: Li Zi-Wei , Luo Xiao-Juan , Liu Fei , Liu Xu-Rui , Shu Xin-Peng , Tong Yue , Lv Quan , Liu Xiao-Yu , Zhang Wei , Peng Dong
Published: April 2024

BACKGROUND

This study aimed to evaluate the safety of enhanced recovery after surgery (ERAS) in elderly patients with gastric cancer (GC).

AIM

To evaluate the safety of ERAS in elderly patients with GC.

METHODS

The PubMed, EMBASE, and Cochrane Library databases were used to search for eligible studies from inception to April 1, 2023. The mean difference (MD), odds ratio (OR) and 95% confidence interval (95%CI) were pooled for analysis. The quality of the included studies was evaluated using the Newcastle-Ottawa Scale scores. We used Stata (V.16.0) software for data analysis.

RESULTS

This study consists of six studies involving 878 elderly patients. By analyzing the clinical outcomes, we found that the ERAS group had shorter postoperative hospital stays (MD = -0.51, I 2 = 0.00%, 95%CI = -0.72 to -0.30, P = 0.00); earlier times to first flatus (defecation; MD = -0.30, I² = 0.00%, 95%CI = -0.55 to -0.06, P = 0.02); less intestinal obstruction (OR = 3.24, I 2 = 0.00%, 95%CI = 1.07 to 9.78, P = 0.04); less nausea and vomiting (OR = 4.07, I 2 = 0.00%, 95%CI = 1.29 to 12.84, P = 0.02); and less gastric retention (OR = 5.69, I 2 = 2.46%, 95%CI = 2.00 to 16.20, P = 0.00). Our results showed that the conventional group had a greater mortality rate than the ERAS group (OR = 0.24, I 2 = 0.00%, 95%CI = 0.07 to 0.84, P = 0.03). However, there was no statistically significant difference in major complications between the ERAS group and the conventional group (OR = 0.67, I 2 = 0.00%, 95%CI = 0.38 to 1.18, P = 0.16).

CONCLUSION

Compared to those with conventional recovery, elderly GC patients who received the ERAS protocol after surgery had a lower risk of mortality.
Vehicle-in-Virtual-Environment Method for ADAS and Connected and Automated Driving Function Development, Demonstration and Evaluation
Authors: Cao Xincheng , Chen Haochong , Gelbal Sukru Yaren , Aksun Guvenc Bilin , Guvenc Levent
Published: April 2024

The current approach for new Advanced Driver Assistance System (ADAS) and Connected and Automated Driving (CAD) function development involves a significant amount of public road testing which is inefficient due to the number miles that need to be driven for rare and extreme events to take place, thereby being very costly also, and unsafe as the rest of the road users become involuntary test subjects. A new development, evaluation and demonstration method for safe, efficient, and repeatable development, demonstration and evaluation of ADAS and CAD functions called Vehicle-in-Virtual –Environment (VVE) was recently introduced as a solution to this problem. The vehicle is operated in a large, empty, and flat area during VVE while its localization and perception sensor data is fed from the virtual environment with other traffic and rare and extreme events being generated as needed. The virtual environment can be easily configured and modified to construct different testing scenarios on demand. This paper focuses on the VVE approach and introduces the coordinate transformations needed to sync pose (location and orientation) in the virtual and physical worlds and handling of localization and perception sensor data using the highly realistic 3D simulation model of a recent autonomous shuttle deployment site in Columbus, Ohio as the virtual world. As a further example that uses multiple actors, the use of VVE for Vehicle-to-VRU communication based Vulnerable Road User (VRU) safety is presented in the paper using VVE experiments and real pedestrian(s) in a safe and repeatable manner. VVE experiments are used to demonstrate the efficacy of the method.
Vehicle Dynamics Model for Simulation Use with Autoware.AI on ROS
Authors: Zeits Roger , Guenther Dennis , Canova Marcello , Heydinger Gary , Sundararaman Venkateshwara Kanna , Salaani Kamel , Elsasser Devin
Published: April 2024

This research focused on developing a methodology for a vehicle dynamics model of a passenger vehicle outfitted with an aftermarket Automated Driving System software package using only literature and track based results. This package consisted of Autoware.AI (Autoware ®) operating on Robot Operating System 1 (ROS™) with C++ and Python ®. Initial focus was understanding the basics of ROS and how to implement test scenarios in Python to characterize the control systems and dynamics of the vehicle. As understanding of the system continued to develop, test scenarios were adapted to better fit system characterization goals with identification of system configuration limits. Trends from on-track testing were identified and paired with first-order linear systems to simulate physical vehicle responses to given command inputs. Sub-models were developed and simulated in MATLAB ® with command inputs from on-track testing. These sub-models were converted into Python then uploaded into the simulation framework. Sub-model dynamics were validated independently of each other and within expected operating ranges. System configuration restricts operation to be within the ranges of linear vehicle dynamics. This work provides a potential methodology for model development that can be expanded upon with future simulation work. The existing system configuration can be altered to allow for various combinations of vehicle operating characteristics. Additionally, this work can fast-track model development for other automated vehicle analyses.
A Method for Evaluating the Complexity of Autonomous Driving Road Scenes
Authors: Ni Hailong , Wu Jian
Published: April 2024

An autonomous vehicle is a comprehensive intelligent system that includes environment sensing, vehicle localization, path planning and decision-making control, of which environment sensing technology is a prerequisite for realizing autonomous driving. In the early days, vehicles sensed the surrounding environment through sensors such as cameras, radar, and lidar. With the development of 5G technology and the Vehicle-to-everything (V2X), other information from the roadside can also be received by vehicles. Such as traffic jam ahead, construction road occupation, school area, current traffic density, crowd density, etc. Such information can help the autonomous driving system understand the current driving environment more clearly. Vehicles are no longer limited to areas that can be sensed by sensors. Vehicles with different autonomous driving levels have different adaptability to the environment. If the current driving environment can be evaluated based on the above information, it can help the vehicle determine whether it needs to exit autonomous driving state. Road scene complexity evaluation is a comprehensive result that needs to consider all factors that may affect vehicle driving. Currently, there are no internationally harmonized evaluation methods or indicators. Many scholars determine scene complexity based on mathematical models or subjective evaluations. This paper proposes a neural network-based road scene complexity assessment method to obtain stable assessment results through data drive. We disassemble the scene elements into road elements, human elements, and traffic flow elements, and use these elements as inputs to a neural network that outputs a series of quantities related to the complexity of the scene. Finally, these outputs are weighted and summed to obtain the final road scene complexity score. The method proposed in this paper can largely get rid of the subjective evaluation of scene complexity, and with this method, we can add arbitrary elements (e.g., weather elements, electromagnetic environment elements) to make an evaluation of scene complexity.
Design, Prototyping, and Implementation of a Vehicle-to-Infrastructure (V2I) System for Eco-Approach and Departure through Connected and Smart Corridors
Authors: Chowduri Suhrit , Midlam-Mohler Shawn , Singh Karun Prateek
Published: April 2024

The advent of Vehicle-to-Everything (V2X) communication has revolutionized the automotive industry, particularly with the rise of Advanced Driver Assistance Systems (ADAS). V2X enables vehicles to communicate not only with each other (V2V) but also with infrastructure (V2I) and pedestrians (V2P), enhancing road safety and efficiency. ADAS, which includes features like adaptive cruise control and automatic intersection navigation, relies on V2X data exchange to make real-time decisions and improve driver assistance capabilities. Over the years, the progress of V2X technology has been marked by standardization efforts, increased deployment, and a growing ecosystem of connected vehicles, paving the way for safer and more efficient automated navigation.

The EcoCAR Mobility Challenge was a 4-year student competition among 12 universities across the United States and Canada sponsored by the U.S. Department of Energy, MathWorks, and General Motors, where each team received a 2019 Chevrolet Blazer from General Motors and was tasked with achieving SAE Level 2 automation to increase the vehicle’s energy efficiency, performance, and connectivity, among other features. Specifically, teams were challenged to add V2I connectivity, requiring the ability to transmit Basic Safety Messages (BSMs) with real-time vehicle information and receive BSMs from other vehicles and SPaT/MAP (Signal Phase and Timing/Map Data) data from Roadside Units (RSUs). This integration of V2X messages (BSM/SPaT and MAP) in cooperative driving systems enhances overall road safety by providing real-time, detailed information about the conditions and intentions of vehicles, fostering a more secure and efficient transportation ecosystem.

The Ohio State University’s EcoCAR’s Connected and Automated Vehicles (CAVs) Sub team was able to implement V2I technology successfully using Cohda Wireless MK5 DSRC (Dedicated Short Range Communication) On-Board Unit (OBU) connected with a Mobilemark MGW-303 antenna, combining two 5.9GHz antennas for DSRC with an active GNSS antenna.
A New U-Net Speech Enhancement Framework Based on Correlation Characteristics of Speech
Authors: Zhang Lijun , Pei Kaikun , Li Wenbo , Meng Dejian , He Yinzhi
Published: April 2024

As a key component of in-vehicle intelligent voice technology, speech enhancement can extract clean speech signals contaminated by environmental noise to improve the perceptual quality and intelligibility of speech. It has extensive applications in the field of intelligent car cabins. Although some end-to-end speech enhancement methods based on time domain have been proposed, there is often limited consideration given to designing model architectures based on the characteristics of the speech signal. In this paper, we propose a new U-Net based speech enhancement framework that utilizes the temporal correlation of speech signals to reconstruct higher-quality and more intelligible clean speech. Firstly, to address the issue of inadequate extraction of multi-scale correlation features from speech signals during feature extraction and reconstruction, a novel dense connection multi-scale feature extraction module based on gated dilated convolution is devised to enhance temporal receptive length and extract diverse scale features effectively. Secondly, in order to tackle the problem of feature loss and harmonic distortion during sampling, a sophisticated pooling-reconstruction fine-grained sampling method based on feature map recombination is proposed. This method aims to minimize information loss during down-sampling while simultaneously enhancing the clarity of reconstructed waveforms during up-sampling. Lastly, leveraging the aforementioned pooling-reconstruction sampling method, we propose a deep supervision approach for multi-scale feature. This approach effective supervision of perception characteristics across different frequency ranges. In order to validate the effectiveness of the proposed framework, experiments were conducted on the Voicebank+Demand dataset. The results show that compared to other advanced algorithms, the proposed model significantly improves metrics such as PESQ, STOI, CSIG, CBAK, and COVL. Even in low SNR environments, the enhanced speech signals exhibit noticeable improvements in quality and intelligibility. This is beneficial for subsequent automotive voice applications.
Data Driven Vehicle Dynamics System Identification Using Gaussian Processes
Authors: Sathe Sumedh , Samak Chinmay , Samak Tanmay , Joglekar Ajinkya , Ranganathan Shyam , Krovi Venkat N
Published: April 2024

Modeling uncertainties pose a significant challenge in the development and deployment of model-based vehicle control systems. Most model- based automotive control systems require the use of a well estimated vehicle dynamics prediction model. The ability of first principles-based models to represent vehicle behavior becomes limited under complex scenarios due to underlying rigid physical assumptions. Additionally, the increasing complexity of these models to meet ever-increasing fidelity requirements presents challenges for obtaining analytical solutions as well as control design. Alternatively, deterministic data driven techniques including but not limited to deep neural networks, polynomial regression, Sparse Identification of Nonlinear Dynamics (SINDy) have been deployed for vehicle dynamics system identification and prediction. However, under real-world conditions which are often uncertain or time varying, including, but not limited to changing terrain and/or physical, a single time-invariant physics- based or parametric model may not accurately represent vehicle behavior resulting in sub-optimal controller performance. The previously mentioned data-driven system identification techniques, by virtue of being deterministic cannot express these uncertainties, leading to a need for multiple models, or a distribution of models to describe vehicle behavior. Gaussian Process Regression constitutes a cogent approach for capturing and expressing modeling uncertainties through a probability distribution. In this paper, we demonstrate Gaussian Process Regression as an able technique for modeling uncertain vehicle dynamics using a real-world vehicle dataset, acquired by performing benchmark maneuvers using a scaled vehicle observed by a motion-capture system. Using Gaussian Process Regression, we develop single-step as well as multi-step prediction models that are usable for reactive as well as predictive model-based control techniques.
A Study of Charge Point Infrastructure Policies on EV Driver Satisfaction
Authors: Fussey Peter , Akin-Onigbinde Akintomiwa , Skarvelis-Kazakos Spyros
Published: April 2024

This paper presents a simulation approach to assess the impact of changes to the charge point infrastructure and policies on Electric Vehicle (EV) user satisfaction, combining both market drivers with the practicalities of EV usage. An agent-based model (ABM) approach is developed where a large number of EVs, that represent the user population, drive within a region of interest. By simulating the driver’s response to their charging experience, the model allows large scale trends to emerge from the population to guide infrastructure policies as the number of EVs increases beyond the initial early adopter market.

The model incorporates a Monte Carlo approach to generate EV and driver agent instances with distinct characteristics, including battery size, vehicle type, driving style, sensitivity to range. The driver model is constructed to respond to events that may increase range anxiety, e.g. increasing the likelihood of charging as the driver becomes more anxious.

A charge point infrastructure and EV population scenario is simulated, including a queuing system for charge stations. The impact on EV driver satisfaction of new policies, including the number of charge points, power rating of charge points, pricing models, green energy providers and numbers of EVs is simulated. The driver satisfaction is assessed by combining a number of metrics, e.g. range anxiety metric, time spent in queues through to access to the desired brand or green energy.

As the number of EVs increase, the policies need to focus on the efficient use of existing charge points to maintain customer satisfaction. The study uses the results to consider the balance between the minimum requirements and value enhancing requirements for customer satisfaction.
Advanced Driver Assistance System (ADAS) Performance Variability with Partial Overlap Targets
Authors: Scally Sean , Paradiso Marc , Koszegi Giacomo , Easter Casey , Kuykendal Michelle , Alexander Ross
Published: April 2024

While various Advanced Driver Assistance System (ADAS) features have become more prevalent in passenger vehicles, their ability to potentially avoid or mitigate vehicle crashes has limitations. Due to current technological limitations, forward collision mitigation technologies such as Forward Collision Warning (FCW) and Automated Emergency Braking (AEB) lack the ability to consistently perform in many unique and challenging scenarios. These limitations are often outlined in driver manuals for ADAS equipped vehicles. One such scenario is the case of a stationary lead vehicle at the side of the road. This is generally considered to be a challenging scenario for FCW and AEB to address because it can often be difficult for the system to discern this threat accurately and consistently from non-threatening roadway infrastructure without unnecessary or nuisance system activations. This is made more difficult when the stationary lead vehicle is only partially in the driving lane and not directly in the forward path, as is the case in the current FCW and AEB confirmation test protocols used by the National Highway Traffic Safety Administration (NHTSA). Partial overlap tests are carried out by Euro New Car Assessment Program (NCAP), however data has not been published to date showing the effect this has on performance.

A test series was designed to investigate the effect of variable overlap, with stationary lead vehicle targets, on the triggering and timing of warnings presented by forward collision mitigation technology.
A Hazard Analysis Approach for Automated Driving Shared Control
Authors: Monkhouse Helen Elizabeth , Ward David
Published: April 2024

Systems-Theoretic Process Analysis (STPA) is being used as a hazard analysis technique within automotive, due in part to its systems engineering viewpoint making it suitable to automated driving feature analysis and with several new and emerging standards and guidelines suggesting its use as one option its familiarity is increasing. Approaches incorporating the human into the STPA Control Structure Diagram (CSD) have been proposed, such as Engineering for Humans: A New Extension to STPA [1]. Such approaches position the human as the top controller in the CSD hierarchy. While placing the human at the top of the CSD is suited to reasoning about supervisory human machine interactions, perhaps in an industrial control setting, we argue that a different approach is needed to address automotive shared control. In an automotive context the driver is integral to vehicle control. Even for vehicle features delivering partial or conditional automation, low level vehicle control tasks may be shared between the driver and the automation. For example, Lane Keep Assistance (LKA) haptic lateral shared control or steer-by-wire input-mixing lateral shared control. In such situations human and machine control is shared between high-level supervisory tasks and lower-level manoeuvring and control tasks. This necessitates modelling the driver differently within the STPA CSD. In this paper we present a vehicle control model and STPA inspired method, which when used together can help the analyst reason about the nature of shared control and potential hazard causes in an automated driving context.