Nevin Manimala

Gan To Kagaku Ryoho. 2023 May;50(5):577-583.

ABSTRACT

Traditionally, the 3 major standard treatments for cancers-surgery, chemotherapy, and radiation therapy-have been applied and have saved many lives. However, malignancies have been the leading cause of death in Japan for more than 40 years since 1981, and the trend is still accelerating. According to the statistics report from the Ministry of Health, Labour and Welfare, cancers accounted for 26.5% of all deaths in 2021, which means that about 1 in 3.5 of all deaths in Japan was due to cancers. Additionally, medical expenditure spent on the diagnosis and treatments for cancer patients have significantly increased, contributing to the pressure on the Japanese economy. Therefore, there is a demand to develop the novel technologies concerning diagnostic methods, effective treatment, and recurrence prevention of cancers. Chimeric antigen receptor(CAR)-T cell therapy has attracted much attention as the next generation of cancer immunotherapy following immune checkpoint blockade therapy, which was the subject of the 2018 Nobel Prize in Physiology or Medicine. CAR-T cell therapy was first approved in the United States in 2017, followed by the EU in 2018 and Japan in March 2019, after having demonstrated significant therapeutic efficacies against B-cell malignancies in clinical trials. However, current CAR-T cell therapies are not yet complete, and there still remain challenges to be overcome. In particular, it is one of the most important issues that current CAR-T cell therapies do not work effectively against solid cancers, which make up the majority of malignant tumors. This review provides an overview of the development toward establishing the next generation CAR-T cell therapy with therapeutic potential against solid cancers.

PMID:37218315

Int J Spine Surg. 2023 May 22:8474. doi: 10.14444/8474. Online ahead of print.

ABSTRACT

BACKGROUND: Retrospective observational study of prospectively collected outcomes.

OBJECTIVE: The use of transpedicular screws augmented with polymethyl methacrylate (PMMA) is an alternative for patients with osteoporotic vertebrae. To investigate whether using PMMA-augmented screws in patients undergoing elective instrumented spinal fusion (ISF) is correlated with an increased risk of infection and the long-term survival of these spinal implants after surgical site infection (SSI).

METHODS: We studied 537 consecutive patients who underwent ISF at some point within a 9-year period, involving a total of 2930 PMMA-augmented screws. Patients were classified into groups: (1) those whose infection was cured with irrigation, surgical debridement, and antibiotic treatment; (2) those whose infection was cured by hardware removal or replacement; and (3) those in whom treatment failed.

RESULTS: Twenty eight of the 537 patients (5.2%) developed SSI after ISF. An SSI developed after primary surgery in 19 patients (4.6%) and after revision surgery in 9 (7.25%). Eleven patients (39.3%) were infected with gram-positive bacteria, 7 (25%) with gram-negative bacteria, and 10 (35.7%) with multiple pathogens. By 2 years after surgery, infection had been cured in 23 patients (82.15%). Although there were no statistically significant differences in infection incidence between preoperative diagnoses (P = 0.178), the need to remove hardware for infection control was almost 80% lower in patients with degenerative disease. All screws were safely explanted while vertebral integrity was maintained. PMMA was not removed, and no recementing was done for new screws.

CONCLUSIONS: The success rate for treatment of deep infection after cemented spinal arthrodesis is high. Infection rate findings and the most commonly found pathogens do not differ between cemented and noncemented fusion. It does not appear that the use of PMMA in cementing vertebrae plays a pivotal role in the development of SSIs.

PMID:37217274 | DOI:10.14444/8474

BMJ Open. 2023 May 22;13(5):e072374. doi: 10.1136/bmjopen-2023-072374.

ABSTRACT

OBJECTIVES: In the process of scientific progress, prior evidence is both relied on and supplanted by new discoveries. We use the term ‘knowledge half-life’ to refer to the phenomenon in which older knowledge is discounted in favour of newer research. By quantifying the knowledge half-life, we sought to determine whether research published in more recent years is preferentially cited over older research in medical and scientific articles.

DESIGN: An observational study employing a directed, systematic search of current literature.

DATA SOURCES: BMJ, PNAS, JAMA, NEJM, The Annals of Internal Medicine, The Lancet, Science and Nature were searched.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Eight high-impact medical and scientific journals were sampled examining original research articles from the first issue of every year over a 25-year span (1996-2020). The outcome of interest was the difference between the publication year of the article and references cited, termed ‘citation lag’.

DATA EXTRACTION AND SYNTHESIS: Analysis of variance was used to identify significant differences in citation lag.

RESULTS: A total of 726 articles and 17 895 references were included with a mean citation lag of 7.5±8.4 years. Across all journals, >70% of references had been published within 10 years of the citing article. Approximately 15%-20% of referenced articles were 10-19 years old, and articles more than 20 years old were cited infrequently. Medical journals articles had references with significantly shorter citation lags compared with general science journals (p≤0.01). Articles published before 2009 had references with significantly shorter citation lags compared with those published in 2010-2020 (p<0.001).

CONCLUSIONS: This study found evidence of a small increase in the citation of older research in medical and scientific literature over the past decade. This phenomenon deserves further characterisation and scrutiny to ensure that ‘old knowledge’ is not being lost.

PMID:37217270 | DOI:10.1136/bmjopen-2023-072374

BMJ Open. 2023 May 22;13(5):e066560. doi: 10.1136/bmjopen-2022-066560.

ABSTRACT

OBJECTIVE: InterVA-5 is a new version of an analytical tool for cause of death (COD) analysis at the population level. This study validates the InterVA-5 against the medical review method, using mortality data in Papua New Guinea (PNG).

DESIGN AND SETTING: This study used mortality data collected from January 2018 to December 2020 in eight surveillance sites of the Comprehensive Health and Epidemiological Surveillance System (CHESS), established by the PNG Institute of Medical Research in six major provinces.

METHODS: The CHESS demographic team conducted verbal autopsy (VA) interviews with close relatives of the deceased, who died in communities within the catchment areas of CHESS, using the WHO 2016 VA instrument. COD of the deceased was assigned by InterVA-5 tool, and independently certified by the medical team. Consistency, difference and agreement between the InterVA-5 model and medical review were assessed. Sensitivity and positive predictive value (PPV) of the InterVA-5 tool were calculated with reference to the medical review method.

RESULTS: Specific COD of 926 deceased people was included in the validation. Agreement between the InterVA-5 tool and medical review was high (kappa test: 0.72; p<0.01). Sensitivity and PPV of the InterVA-5 were 93% and 72% for cardiovascular diseases, 84% and 86% for neoplasms, 65% and 100% for other chronic non-communicable diseases (NCDs), and 78% and 64% for maternal deaths, respectively. For infectious diseases and external CODs, sensitivity and PPV of the InterVA-5 were 94% and 90%, respectively, while the sensitivity and PPV of the medical review method were both 54% for classifying neonatal CODs.

CONCLUSION: The InterVA-5 tool works well in the PNG context to assign specific CODs of infectious diseases, cardiovascular diseases, neoplasms and injuries. Further improvements with respect to chronic NCDs, maternal deaths and neonatal deaths are needed.

PMID:37217264 | DOI:10.1136/bmjopen-2022-066560

BMJ Open. 2023 May 22;13(5):e067386. doi: 10.1136/bmjopen-2022-067386.

ABSTRACT

OBJECTIVES: REVEAL-CKD aims to estimate the prevalence of, and factors associated with, undiagnosed stage 3 chronic kidney disease (CKD).

DESIGN: Multinational, observational study.

SETTING: Data from six country-specific electronic medical records and/or insurance claims databases from five countries (France, Germany, Italy, Japan and the USA [two databases]).

PARTICIPANTS: Eligible participants (≥18 years old) had ≥2 consecutive estimated glomerular filtration rate (eGFR) measurements (calculated from serum creatinine values, sex and age) taken from 2015 onwards that were indicative of stage 3 CKD (≥30 and <60 mL/min/1.73 m²). Undiagnosed cases lacked an International Classification of Diseases 9/10 diagnosis code for CKD (any stage) any time before, and up to 6 months after, the second qualifying eGFR measurement (study index).

MAIN OUTCOME MEASURES: The primary outcome was point prevalence of undiagnosed stage 3 CKD. Time to diagnosis was assessed using the Kaplan-Meier approach. Factors associated with lacking a CKD diagnosis and risk of diagnostic delay were assessed using logistic regression adjusted for baseline covariates.

RESULTS: The prevalence of undiagnosed stage 3 CKD was 95.5% (19 120/20 012 patients) in France, 84.3% (22 557/26 767) in Germany, 77.0% (50 547/65 676) in Italy, 92.1% (83 693/90 902) in Japan, 61.6% (13 845/22 470) in the US Explorys Linked Claims and Electronic Medical Records Data database and 64.3% (161 254/250 879) in the US TriNetX database. The prevalence of undiagnosed CKD increased with age. Factors associated with undiagnosed CKD were female sex (vs male, range of odds ratios across countries: 1.29-1.77), stage 3a CKD (vs 3b, 1.81-3.66), no medical history (vs a history) of diabetes (1.26-2.77) or hypertension (1.35-1.78).

CONCLUSIONS: There are substantial opportunities to improve stage 3 CKD diagnosis, particularly in female patients and older patients. The low diagnosis rates in patients with comorbidities that put them at risk of disease progression and complications require attention.

TRIAL REGISTRATION: NCT04847531.

PMID:37217263 | DOI:10.1136/bmjopen-2022-067386

Tob Control. 2023 May 22:tc-2022-057624. doi: 10.1136/tc-2022-057624. Online ahead of print.

ABSTRACT

OBJECTIVE: To compare 50-year forecasts of Australian tobacco smoking rates in relation to trends in smoking initiation and cessation and in relation to a national target of ≤5% adult daily prevalence by 2030.

METHODS: A compartmental model of Australian population daily smoking, calibrated to the observed smoking status of 229 523 participants aged 20-99 years in 26 surveys (1962-2016) by age, sex and birth year (1910-1996), estimated smoking prevalence to 2066 using Australian Bureau of Statistics 50-year population predictions. Prevalence forecasts were compared across scenarios in which smoking initiation and cessation trends from 2017 were continued, kept constant or reversed.

RESULTS: At the end of the observation period in 2016, model-estimated daily smoking prevalence was 13.7% (90% equal-tailed interval (EI) 13.4%-14.0%). When smoking initiation and cessation rates were held constant, daily smoking prevalence reached 5.2% (90% EI 4.9%-5.5%) after 50 years, in 2066. When initiation and cessation rates continued their trajectory downwards and upwards, respectively, daily smoking prevalence reached 5% by 2039 (90% EI 2037-2041). The greatest progress towards the 5% goal came from eliminating initiation among younger cohorts, with the target met by 2037 (90% EI 2036-2038) in the most optimistic scenario. Conversely, if initiation and cessation rates reversed to 2007 levels, estimated prevalence was 9.1% (90% EI 8.8%-9.4%) in 2066.

CONCLUSION: A 5% adult daily smoking prevalence target cannot be achieved by the year 2030 based on current trends. Urgent investment in concerted strategies that prevent smoking initiation and facilitate cessation is necessary to achieve 5% prevalence by 2030.

PMID:37217260 | DOI:10.1136/tc-2022-057624

Genome Res. 2023 May 22:gr.277674.123. doi: 10.1101/gr.277674.123. Online ahead of print.

ABSTRACT

The collection and sharing of genomic data are becoming increasingly commonplace in research, clinical, and direct-to-consumer settings. The computational protocols typically adopted to protect individual privacy include sharing summary statistics, such as allele frequencies, or limiting query responses to the presence/absence of alleles of interest using web-services called Beacons. However, even such limited releases are susceptible to likelihood-ratio-based membership-inference attacks. Several approaches have been proposed to preserve privacy, which either suppress a subset of genomic variants or modify query responses for specific variants (e.g., adding noise, as in differential privacy). However, many of these approaches result in a significant utility loss, either suppressing many variants or adding a substantial amount of noise. In this paper, we introduce optimization-based approaches to explicitly trade off the utility of summary data or Beacon responses and privacy with respect to membership-inference attacks based on likelihood-ratios, combining variant suppression and modification. We consider two attack models. In the first, an attacker applies a likelihood-ratio test to make membership-inference claims. In the second model, an attacker uses a threshold that accounts for the effect of the data release on the separation in scores between individuals in the dataset and those who are not. We further introduce highly scalable approaches for approximately solving the privacy-utility tradeoff problem when information is either in the form of summary statistics or presence/absence queries. Finally, we show that the proposed approaches outperform the state of the art in both utility and privacy through an extensive evaluation with public datasets.

PMID:37217251 | DOI:10.1101/gr.277674.123

Genome Res. 2023 May 22:gr.277677.123. doi: 10.1101/gr.277677.123. Online ahead of print.

ABSTRACT

The assay for transposase-accessible chromatin with sequencing (ATAC-seq) is a common assay to identify chromatin accessible regions by using a Tn5 transposase that can access, cut, and ligate adapters to DNA fragments for subsequent amplification and sequencing. These sequenced regions are quantified and tested for enrichment in a process referred to as “peak calling”. Most unsupervised peak calling methods are based on simple statistical models and suffer from elevated false positive rates. Newly developed supervised deep learning methods can be successful, but they rely on high quality labeled data for training, which can be difficult to obtain. Moreover, though biological replicates are recognized to be important, there are no established approaches for using replicates in the deep learning tools, and the approaches available for traditional methods either cannot be applied to ATAC-seq, where control samples may be unavailable, or are post-hoc and do not capitalize on potentially complex, but reproducible signal in the read enrichment data. Here, we propose a novel peak caller that uses unsupervised contrastive learning to extract shared signals from multiple replicates. Raw coverage data are encoded to obtain low-dimensional embeddings and optimized to minimize a contrastive loss over biological replicates. These embeddings are passed to another contrastive loss for learning and predicting peaks and decoded to denoised data under an autoencoder loss. We compared our Replicative Contrastive Learner (RCL) method with other existing methods on ATAC-seq data, using annotations from ChromHMM genome and transcription factor ChIP-seq as noisy truth. RCL consistently achieved the best performance.

PMID:37217250 | DOI:10.1101/gr.277677.123

BMJ Health Care Inform. 2023 May;30(1):e100712. doi: 10.1136/bmjhci-2022-100712.

ABSTRACT

OBJECTIVES: Artificial intelligence (AI) is increasingly tested and integrated into breast cancer screening. Still, there are unresolved issues regarding its possible ethical, social and legal impacts. Furthermore, the perspectives of different actors are lacking. This study investigates the views of breast radiologists on AI-supported mammography screening, with a focus on attitudes, perceived benefits and risks, accountability of AI use, and potential impact on the profession.

METHODS: We conducted an online survey of Swedish breast radiologists. As early adopter of breast cancer screening, and digital technologies, Sweden is a particularly interesting case to study. The survey had different themes, including: attitudes and responsibilities pertaining to AI, and AI’s impact on the profession. Responses were analysed using descriptive statistics and correlation analyses. Free texts and comments were analysed using an inductive approach.

RESULTS: Overall, respondents (47/105, response rate 44.8%) were highly experienced in breast imaging and had a mixed knowledge of AI. A majority (n=38, 80.8%) were positive/somewhat positive towards integrating AI in mammography screening. Still, many considered there to be potential risks to a high/somewhat high degree (n=16, 34.1%) or were uncertain (n=16, 34.0%). Several important uncertainties were identified, such as defining liable actor(s) when AI is integrated into medical decision-making.

CONCLUSIONS: Swedish breast radiologists are largely positive towards integrating AI in mammography screening, but there are significant uncertainties that need to be addressed, especially regarding risks and responsibilities. The results stress the importance of understanding actor-specific and context-specific challenges to responsible implementation of AI in healthcare.

PMID:37217249 | DOI:10.1136/bmjhci-2022-100712

J Immunother Cancer. 2023 May;11(5):e006514. doi: 10.1136/jitc-2022-006514.

ABSTRACT

BACKGROUND: Immune checkpoint inhibitors (ICIs)-based therapy, is regarded as one of the major breakthroughs in cancer treatment. However, it is challenging to accurately identify patients who may benefit from ICIs. Current biomarkers for predicting the efficacy of ICIs require pathological slides, and their accuracy is limited. Here we aim to develop a radiomics model that could accurately predict response of ICIs for patients with advanced breast cancer (ABC).

METHODS: Pretreatment contrast-enhanced CT (CECT) image and clinicopathological features of 240 patients with ABC who underwent ICIs-based treatment in three academic hospitals from February 2018 to January 2022 were assigned into a training cohort and an independent validation cohort. For radiomic features extraction, CECT images of patients 1 month prior to ICIs-based therapies were first delineated with regions of interest. Data dimension reduction, feature selection and radiomics model construction were carried out with multilayer perceptron. Combined the radiomics signatures with independent clinicopathological characteristics, the model was integrated by multivariable logistic regression analysis.

RESULTS: Among the 240 patients, 171 from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center were evaluated as a training cohort, while other 69 from Sun Yat-sen University Cancer Center and the First Affiliated Hospital of Sun Yat-sen University were the validation cohort. The area under the curve (AUC) of radiomics model was 0.994 (95% CI: 0.988 to 1.000) in the training and 0.920 (95% CI: 0.824 to 1.000) in the validation set, respectively, which were significantly better than the performance of clinical model (0.672 for training and 0.634 for validation set). The integrated clinical-radiomics model showed increased but not statistical different predictive ability in both the training (AUC=0.997, 95% CI: 0.993 to 1.000) and validation set (AUC=0.961, 95% CI: 0.885 to 1.000) compared with the radiomics model. Furthermore, the radiomics model could divide patients under ICIs-therapies into high-risk and low-risk group with significantly different progression-free survival both in training (HR=2.705, 95% CI: 1.888 to 3.876, p<0.001) and validation set (HR=2.625, 95% CI: 1.506 to 4.574, p=0.001), respectively. Subgroup analyses showed that the radiomics model was not influenced by programmed death-ligand 1 status, tumor metastatic burden or molecular subtype.

CONCLUSIONS: This radiomics model provided an innovative and accurate way that could stratify patients with ABC who may benefit more from ICIs-based therapies.

PMID:37217246 | DOI:10.1136/jitc-2022-006514