Advances in Computational Biology for Diagnosing Neurodegenerative Diseases: A Comprehensive Review.

The numerous and varied forms of neurodegenerative illnesses provide a considerable challenge to contemporary healthcare. The emergence of artificial intelligence has fundamentally changed the diagnostic picture by providing effective and early means of identifying these crippling illnesses. As a subset of computational intelligence, machine-learning algorithms have become very effective tools for the analysis of large datasets that include genetic, imaging, and clinical data. Moreover, multi-modal data integration, which includes information from brain imaging (MRI, PET scans), genetic profiles, and clinical evaluations, is made easier by computational intelligence. A thorough knowledge of the course of the illness is made possible by this consolidative method, which also facilitates the creation of predictive models for early medical evaluation and outcome prediction. Furthermore, there has been a great deal of promise shown by the use of artificial intelligence to neuroimaging analysis. Sophisticated image processing methods combined with machine learning algorithms make it possible to identify functional and structural anomalies in the brain, which often act as early indicators of neurodegenerative diseases. This chapter examines how computational intelligence plays a critical role in improving the diagnosis of neurodegenerative diseases such as Parkinson's, Alzheimer's, etc. To sum up, computational intelligence provides a revolutionary approach for improving the identification of neurodegenerative illnesses. In the battle against these difficult disorders, embracing and improving these computational techniques will surely pave the path for more individualized therapy and more therapies that are successful.

A techno-economic perspective on efficient hybrid renewable energy solutions in Douala, Cameroon's grid-connected systems.

Cameroon is currently grappling with a significant energy crisis, which is adversely affecting its economy due to cost, reliability, and availability constraints within the power infrastructure. While electrochemical storage presents a potential remedy, its implementation faces hurdles like high costs and technical limitations. Conversely, generator-based systems, although a viable alternative, bring their own set of issues such as noise pollution and demanding maintenance requirements. This paper meticulously assesses a novel hybrid energy system specifically engineered to meet the diverse energy needs of Douala, Cameroon. By employing advanced simulation techniques, especially the Hybrid Optimization Model for Electric Renewable (HOMER) Pro program, the study carefully examines the intricacies of load demands across distinct consumer categories while accommodating varied pricing models. The paper offers a detailed analysis of the proposed grid-connected PV/Diesel/Generator system, aiming to gauge its performance, economic feasibility, and reliability in ensuring uninterrupted energy supply. Notably, the study unveils significant potential for cost reduction per kilowatt-hour, indicating promising updated rates of $0.07/kW, $0.08/kW, and $0.06/kW for low, medium, and high usage groups, respectively. Furthermore, the research underscores the importance of overcoming operational challenges and constraints such as temperature fluctuations, equipment costs, and regulatory compliance. It also acknowledges the impact of operational nuances like maintenance and grid integration on system efficiency. As the world progresses towards renewable energy adoption and hybrid systems, this investigation lays a strong foundation for future advancements in renewable energy integration and energy management strategies. It strives to create a sustainable energy ecosystem in Cameroon and beyond, where hybrid energy systems play a pivotal role in mitigating power deficiencies and supporting sustainable development.

State-of-the-art review on energy sharing and trading of resilient multi microgrids.

Independently run single microgrids (MGs) encounter difficulties with inadequate self-consumption of local renewable energy and frequent power exchange with the grid. Combining numerous MGs to form a multi-microgrid (MMG) is a viable approach to enhance smart distribution networks' operational and financial performance. However, the correlation and coordination of intermittent power generation within each MG network pose many techno-economic challenges for energy sharing and trading. This review offers a comprehensive analysis of these challenges within the framework of MMG operations. It examines state-of-the-art methodologies for optimizing multi-energy dispatch and scrutinizes contemporary strategies within energy markets that contribute to the resilience of power systems. The discourse extends to the burgeoning role of blockchain technology in revolutionizing decentralized market frameworks and the intricacies of MMG coordination for reliable and cost-effective energy distribution. Overall, this study provides ample inspiration for theoretical and practical research to the new entrants and experts alike to develop new concepts for energy markets, scheduling and novel operating models for future resilient multi-energy networked systems/MMGs.

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience.

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control.

This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions.

A novel strategy towards efficient and reliable electric vehicle charging for the realisation of a true sustainable transportation landscape.

This paper proposes an innovative approach for improving the charging efficiency of electric vehicles (EVs) by combining photovoltaic (PV) systems with AC-DC Power Factor Correction (PFC). The proposed approach employs bi-directional power flow management within the PFC system, allowing for enhanced resource utilization and EV battery capacity under a variety of environmental circumstances. A modified Lyapunov-based robust model reference adaptive controller (M-LRMRAC) is developed to provide real-time Maximum Power Point Tracking (MPPT) for the PV array. By quickly recording the MPP, this controller skilfully adjusts to shifting radiation and temperature dynamics. A noteworthy accomplishment is that the M-LRMRAC outperforms traditional Perturb and Observe (P&O) techniques by achieving quick MPP convergence (0.54 s). Additionally, the benefits of this integrated system go beyond effective MPPT. The method achieves operating at unity power factor and reduces total harmonic distortion, which results in improved power quality when charging EV Batteries (EVB). The entire solution provided by this multifaceted architecture improves the quality of electricity delivered to EV batteries while also increasing energy efficiency. This research helps to the evolution of sustainable and dependable EV charging infrastructure by solving difficulties and optimising performance. The combination of PV systems with AC-DC PFC, aided by the M-LRMRAC technology, presents a viable route for attaining efficient, clean, and high-quality EV charging, hence supporting the shift to a greener and more sustainable transportation landscape.

Serum hepcidin levels in chronic liver disease: a systematic review and meta-analysis.

OBJECTIVES: Dysregulation of hepcidin-iron axis is presumed to account for abnormal iron status in patients with chronic liver disease (CLD). Our aim is to determine the effect of specific etiologies of CLD and of cirrhosis on serum hepcidin levels. METHODS: PubMed, Embase, Web of Science were searched for studies comparing serum hepcidin levels in patients with CLD to that in controls using enzyme-linked immunosorbent assay. The study was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Guidelines. Statistical analysis was carried out with STATA using random effects model to calculate the mean difference (MD) between two groups. RESULTS: Hepcidin levels were significantly lower in subjects with hepatitis C virus (16 studies) [MD -1.6 (95 % CI: -2.66 to -0.54), p<0.01] and alcoholic liver disease (3 studies) [MD -0.84 (95 % CI: -1.6 to -0.07), p=0.03] than controls. Serum hepcidin was significantly higher in subjects with non-alcoholic fatty liver disease (12 studies) [MD 0.62 (95 % CI: 0.21 to 1.03), p<0.01], but did not differ in subjects with hepatitis B and controls (eight studies) [MD -0.65 (95 % CI: -1.47 to 0.16), p=0.12]. Hepcidin levels were significantly lower in patients with cirrhosis of any etiology (four studies) [MD -1.02 (CI: -1.59 to -0.45), p<0.01] vs. controls (CI: confidence interval). CONCLUSIONS: Serum hepcidin levels are altered in common forms of CLD albeit not in a consistent direction. Additional study is needed to determine how changes in hepcidin levels are related to dysregulation of iron metabolism in CLD.

Development and characterisation of suitably bioengineered microfibrillar matrix-based 3D prostate cancer model forin vitrodrug testing.

Bioengineered 3D models that can mimic patient-specific pathologiesin vitroare valuable tools for developing and validating anticancer therapeutics. In this study, microfibrillar matrices with unique structural and functional properties were fabricated as 3D spherical and disc-shaped scaffolds with highly interconnected pores and the potential of the newly developed scaffolds for developing prostate cancer model has been investigated. The newly developed scaffolds showed improved cell retention upon seeding with cancer cells compared to conventional electrospun scaffolds. They facilitated rapid growth and deposition of cancer-specific extracellular matrix through-the-thickness of the scaffold. Compared to the prostate cancer cells grown in 2D culture, the newly developed prostate cancer model showed increased resistance to the chemodrug Docetaxel regardless of the drug concentration or the treatment frequency. A significant reduction in the cell number was observed within one week after the drug treatment in the 2D culture for both PC3 and patient-derived cells. Interestingly, almost 20%-30% of the cancer cells in the newly developed 3D model survived the drug treatment, and the patient-derived cells were more resistant than the tested cell line PC3. The results from this study indicate the potential of the newly developed prostate cancer model forin vitrodrug testing.

A population-scale temporal case-control evaluation of COVID-19 disease phenotype and related outcome rates in patients with cancer in England (UKCCP).

Patients with cancer are at increased risk of hospitalisation and mortality following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, the SARS-CoV-2 phenotype evolution in patients with cancer since 2020 has not previously been described. We therefore evaluated SARS-CoV-2 on a UK populationscale from 01/11/2020-31/08/2022, assessing case-outcome rates of hospital assessment(s), intensive care admission and mortality. We observed that the SARS-CoV-2 disease phenotype has become less severe in patients with cancer and the non-cancer population. Case-hospitalisation rates for patients with cancer dropped from 30.58% in early 2021 to 7.45% in 2022 while case-mortality rates decreased from 20.53% to 3.25%. However, the risk of hospitalisation and mortality remains 2.10x and 2.54x higher in patients with cancer, respectively. Overall, the SARS-CoV-2 disease phenotype is less severe in 2022 compared to 2020 but patients with cancer remain at higher risk than the non-cancer population. Patients with cancer must therefore be empowered to live more normal lives, to see loved ones and families, while also being safeguarded with expanded measures to reduce the risk of transmission.

Optimal alpha-1 antitrypsin level cutoffs for genotype identification in patients with chronic liver disease.

BACKGROUND: Controversy exists whether alpha-1 antitrypsin (A1AT) genotype testing should be performed as a first-line screening for A1AT heterozygous variants. METHODS: We calculated the median and interquartile range of A1AT level for each genotype in 4378 patients with chronic liver disease and "miss rate" of MZ genotype identification at various cutoff levels. FINDINGS: Significant overlap in A1AT level noted with Pi*MM, MZ, and MS variants. Miss rate of Pi*MZ at a cutoff level <100 was 29%, <110 was 18%, <120 was 8%, and <130 was 4%. We suggest simultaneous measurement of A1AT level and genotype in patients with chronic liver disease.

Transcriptional-translational conflict is a barrier to cellular transformation and cancer progression.

We uncover a tumor-suppressive process in urothelium called transcriptional-translational conflict caused by deregulation of the central chromatin remodeling component ARID1A. Loss of Arid1a triggers an increase in a nexus of pro-proliferation transcripts, but a simultaneous inhibition of the eukaryotic elongation factor 2 (eEF2), which results in tumor suppression. Resolution of this conflict through enhancing translation elongation speed enables the efficient and precise synthesis of a network of poised mRNAs resulting in uncontrolled proliferation, clonogenic growth, and bladder cancer progression. We observe a similar phenomenon in patients with ARID1A-low tumors, which also exhibit increased translation elongation activity through eEF2. These findings have important clinical implications because ARID1A-deficient, but not ARID1A-proficient, tumors are sensitive to pharmacologic inhibition of protein synthesis. These discoveries reveal an oncogenic stress created by transcriptional-translational conflict and provide a unified gene expression model that unveils the importance of the crosstalk between transcription and translation in promoting cancer.

The NAFLD Decompensation Risk Score: External Validation and Comparison to Existing Models to Predict Hepatic Events in a Retrospective Cohort Study.

Background: The NAFLD decompensation risk score (the Iowa Model) was recently developed to identify patients with nonalcoholic fatty liver disease (NAFLD) at highest risk of developing hepatic events using three variables-age, platelet count, and diabetes. Aims: We performed an external validation of the Iowa Model and compared it to existing non-invasive models. Methods: We included 249 patients with NAFLD at Boston Medical Center, Boston, Massachusetts, in the external validation cohort and 949 patients in the combined internal/external validation cohort. The primary outcome was the development of hepatic events (ascites, hepatic encephalopathy, esophageal or gastric varices, or hepatocellular carcinoma). We used Cox proportional hazards to analyze the ability of the Iowa Model to predict hepatic events in the external validation (https://uihc.org/non-alcoholic-fatty-liver-disease-decompensation-risk-score-calculator). We compared the performance of the Iowa Model to the AST-to-platelet ratio index (APRI), NAFLD fibrosis score (NFS), and the FIB-4 index in the combined cohort. Results: The Iowa Model significantly predicted the development of hepatic events with hazard ratio of 2.5 [95% confidence interval (CI) 1.7-3.9, P < 0.001] and area under the receiver operating characteristic curve (AUROC) of 0.87 (CI 0.83-0.91). The AUROC of the Iowa Model (0.88, CI: 0.85-0.92) was comparable to the FIB-4 index (0.87, CI: 0.83-0.91) and higher than NFS (0.66, CI: 0.63-0.69) and APRI (0.76, CI: 0.73-0.79). Conclusions: In an urban, racially and ethnically diverse population, the Iowa Model performed well to identify NAFLD patients at higher risk for liver-related complications. The model provides the individual probability of developing hepatic events and identifies patients in need of early intervention.

Ileocolonic intussusception presenting as chronic diarrhea in an elderly woman.

Video 1Colonoscopy findings of cecal mass as a lead point causing intussusception.

Portal vein thrombosis, hepatic decompensation, and survival in patients with porto-sinusoidal vascular disease and portal hypertension.

BACKGROUND: Porto-sinusoidal vascular disease (PSVD) is a novel nomenclature to describe non-cirrhotic portal hypertension and characteristic histology without portal vein thrombosis (PVT). It is a more inclusive definition than the previously well-recognized entity idiopathic non-cirrhotic portal hypertension. There is a paucity of data on PSVD patients. METHODS: A total of 33 patients diagnosed with PSVD and portal hypertension (PH) between 2005 and 2021 were included. Data were retrieved from electronic medical record system and analyzed. RESULTS: Of the 33 patients, 6 (18%) occurred in post-transplant allograft liver. After a median follow-up of 96 months (interquartile range, IQR [52, 139]), 14 deaths occurred (42%), 4 directly related to decompensated liver disease. The Kaplan-Meier survival estimates at 1, 5, and 10 years were 94%, 87% and 58%. PVT occurred in 10 patients (30%). The Nelson-Aalen cumulative risk estimate for PVT at 1, 5 and 10 years were 16%, 25% and 48%. The median model for end-stage liver disease and Child-Pugh score at initial presentation were 8 (IQR [7-12]) and 5 [5-6], and increased to 13 [8, 18] and 7 [5, 8], respectively, at the end of follow-up. Of the 11 patients who presented with splenomegaly and no specific sign of PH, 7 (64%) developed varices and 3 (27%) ascites at a median follow-up of 100 months. CONCLUSIONS: PSVD with PH is not a benign entity. Mortality, PVT and hepatic decompensation are common. Patients with PSVD must be closely monitored, including those who only have non-specific clinical signs (e.g., splenomegaly) of PH.

Effectiveness and Safety of EUS Rendezvous After Failed Biliary Cannulation With ERCP: A Systematic Review and Proportion Meta-analysis.

BACKGROUND: Endoscopic ultrasound-guided rendezvous (EUS-RV) endoscopic retrograde cholangiopancreatography (ERCP) is an alternative to interventional radiology-guided rendezvous ERCP in patients who failed biliary cannulation with conventional ERCP. However, there is significant variation in reported rates of success and adverse events associated with EUS-RV-assisted ERCP. We performed a systematic review and a proportion meta-analysis to reliably assess the effectiveness and safety of the EUS-RV-assisted ERCP. MATERIALS AND METHODS: We conducted a comprehensive search of multiple electronic databases and conference proceedings (from inception through August 2020) to identify studies reporting EUS-RV-assisted ERCP in patients who failed biliary cannulation with conventional ERCP techniques. Using the random-effects model described by DerSimonian and Laird, we calculated the pooled rates of technical success, clinical success, and adverse events of EUS-RV-assisted ERCP. RESULTS: Twelve studies reporting a total of 342 patients were included in the meta-analysis. The pooled rate of technical success (12 studies reporting a total of 342 patients) was 86.1% [95% confidence interval (CI): 78.4-91.3]. The pooled rate of clinical success (4 studies reporting a total of 94 patients) was 80.8% (95% CI: 64.1-90.8). The pooled rate of overall adverse events (12 studies; 42 events in 342 patients) was 14% (95% CI: 10.5-18.4). Low to moderate heterogeneity was noted in the analyses. CONCLUSIONS: EUS-RV-assisted ERCP appears to be effective and safe in patients who failed biliary cannulation with conventional ERCP. Given the risk of adverse events, it should be performed in centers with expertise in therapeutic endoscopic ultrasound.

Alpha-1-Antitrypsin Pi*MZ variant increases risk of developing hepatic events in nonalcoholic fatty liver disease patients.

AIMS: Heterozygous alpha-1-antitrypsin (A1AT) Pi*MZ variant has been shown to increase the risk of developing liver cirrhosis in patients with non-alcoholic fatty liver disease (NAFLD). We aimed to determine the association between heterozygous Pi*MZ and Pi*MS variants and development of hepatic decompensation events in NAFLD patients. METHODS: We included patients with NAFLD who also had A1AT genotyping performed from 2005 to 2020. We recorded demographic and clinical variables, and data on hepatic events (ascites, hepatic encephalopathy, esophageal variceal bleed, or hepatocellular carcinoma), if any. We performed binary logistic regression analysis to assess the association between A1AT variants and hepatic events, and calculated Odds ratio (OR) with their 95% confidence intervals (Cl). RESULTS: We included 1532 patients with NAFLD, of which 1249 patients had Pi*MM, 121 had Pi*MS, and 162 had Pi*MZ. Of the 1532 patients, hepatic events developed in 521 (34%) patients. The percentage of patients with Pi*MZ variant was significantly higher in patients with hepatic events as compared to patients without hepatic events (18.7 % vs 8.1%, p<0.0001). Pi*MZ variant was noted to significantly increase the odds of developing hepatic events in NAFLD patients, unadjusted OR: 1.82 (1.3-2.5, p<0.001), adjusted OR (for age, sex, body mass index, and diabetes mellitus) 1.76 (1.2-2.5, p = 0.002). Pi*MS variant did not increase the odds of hepatic events in NAFLD patients, OR: 0.92 (0.6-1.4, p = 0.70). CONCLUSION: Patients with NAFLD and A1AT Pi*MZ variant are at increased risk for developing hepatic decompensation. NAFLD patients should be offered A1AT genotyping for risk stratification, counseling, and multidisciplinary intervention for NAFLD.

COVID-19: Third dose booster vaccine effectiveness against breakthrough coronavirus infection, hospitalisations and death in patients with cancer: A population-based study.

PURPOSE: People living with cancer and haematological malignancies are at an increased risk of hospitalisation and death following infection with acute respiratory syndrome coronavirus 2. Coronavirus third dose vaccine boosters are proposed to boost waning immune responses in immunocompromised individuals and increase coronavirus protection; however, their effectiveness has not yet been systematically evaluated. METHODS: This study is a population-scale real-world evaluation of the United Kingdom's third dose vaccine booster programme for cancer patients from 8th December 2020 to 7th December 2021. The cancer cohort comprises individuals from Public Health England's national cancer dataset, excluding individuals less than 18 years. A test-negative case-control design was used to assess the third dose booster vaccine effectiveness. Multivariable logistic regression models were fitted to compare risk in the cancer cohort relative to the general population. RESULTS: The cancer cohort comprised of 2,258,553 tests from 361,098 individuals. Third dose boosters were evaluated by reference to 87,039,743 polymerase chain reaction coronavirus tests. Vaccine effectiveness against breakthrough infections, symptomatic infections, coronavirus hospitalisation and death in cancer patients were 59.1%, 62.8%, 80.5% and 94.5%, respectively. Lower vaccine effectiveness was associated with a cancer diagnosis within 12 months, lymphoma, recent systemic anti-cancer therapy (SACT) or radiotherapy. Patients with lymphoma had low levels of protection from symptomatic disease. In spite of third dose boosters, following multivariable adjustment, individuals with cancer remain at an increased risk of coronavirus hospitalisation and death compared to the population control (OR 3.38, 3.01, respectively. p < 0.001 for both). CONCLUSIONS: Third dose boosters are effective for most individuals with cancer, increasing protection from coronavirus. However, their effectiveness is heterogenous and lower than the general population. Many patients with cancer will remain at the increased risk of coronavirus infections even after 3 doses. In the case of patients with lymphoma, there is a particularly strong disparity of vaccine effectiveness against breakthrough infection and severe disease. Breakthrough infections will disrupt cancer care and treatment with potentially adverse consequences on survival outcomes. The data support the role of vaccine boosters in preventing severe disease, and further pharmacological intervention to prevent transmission and aid viral clearance to limit the disruption of cancer care as the delivery of care continues to evolve during the coronavirus pandemic.

The amino acid sensor GCN2 suppresses terminal oligopyrimidine (TOP) mRNA translation via La-related protein 1 (LARP1).

La-related protein 1 (LARP1) has been identified as a key translational inhibitor of terminal oligopyrimidine (TOP) mRNAs downstream of the nutrient sensing protein kinase complex, mTORC1. LARP1 exerts this inhibitory effect on TOP mRNA translation by binding to the mRNA cap and the adjacent 5'TOP motif, resulting in the displacement of the cap-binding protein eIF4E from TOP mRNAs. However, the involvement of additional signaling pathway in regulating LARP1-mediated inhibition of TOP mRNA translation is largely unexplored. In the present study, we identify a second nutrient sensing kinase GCN2 that converges on LARP1 to control TOP mRNA translation. Using chromatin-immunoprecipitation followed by massive parallel sequencing (ChIP-seq) analysis of activating transcription factor 4 (ATF4), an effector of GCN2 in nutrient stress conditions, in WT and GCN2 KO mouse embryonic fibroblasts, we determined that LARP1 is a GCN2-dependent transcriptional target of ATF4. Moreover, we identified GCN1, a GCN2 activator, participates in a complex with LARP1 on stalled ribosomes, suggesting a role for GCN1 in LARP1-mediated translation inhibition in response to ribosome stalling. Therefore, our data suggest that the GCN2 pathway controls LARP1 activity via two mechanisms: ATF4-dependent transcriptional induction of LARP1 mRNA and GCN1-mediated recruitment of LARP1 to stalled ribosomes.

Vaccine effectiveness against COVID-19 breakthrough infections in patients with cancer (UKCCEP): a population-based test-negative case-control study.

BACKGROUND: People with cancer are at increased risk of hospitalisation and death following infection with SARS-CoV-2. Therefore, we aimed to conduct one of the first evaluations of vaccine effectiveness against breakthrough SARS-CoV-2 infections in patients with cancer at a population level. METHODS: In this population-based test-negative case-control study of the UK Coronavirus Cancer Evaluation Project (UKCCEP), we extracted data from the UKCCEP registry on all SARS-CoV-2 PCR test results (from the Second Generation Surveillance System), vaccination records (from the National Immunisation Management Service), patient demographics, and cancer records from England, UK, from Dec 8, 2020, to Oct 15, 2021. Adults (aged ≥18 years) with cancer in the UKCCEP registry were identified via Public Health England's Rapid Cancer Registration Dataset between Jan 1, 2018, and April 30, 2021, and comprised the cancer cohort. We constructed a control population cohort from adults with PCR tests in the UKCCEP registry who were not contained within the Rapid Cancer Registration Dataset. The coprimary endpoints were overall vaccine effectiveness against breakthrough infections after the second dose (positive PCR COVID-19 test) and vaccine effectiveness against breakthrough infections at 3-6 months after the second dose in the cancer cohort and control population. FINDINGS: The cancer cohort comprised 377 194 individuals, of whom 42 882 had breakthrough SARS-CoV-2 infections. The control population consisted of 28 010 955 individuals, of whom 5 748 708 had SARS-CoV-2 breakthrough infections. Overall vaccine effectiveness was 69·8% (95% CI 69·8-69·9) in the control population and 65·5% (65·1-65·9) in the cancer cohort. Vaccine effectiveness at 3-6 months was lower in the cancer cohort (47·0%, 46·3-47·6) than in the control population (61·4%, 61·4-61·5). INTERPRETATION: COVID-19 vaccination is effective for individuals with cancer, conferring varying levels of protection against breakthrough infections. However, vaccine effectiveness is lower in patients with cancer than in the general population. COVID-19 vaccination for patients with cancer should be used in conjunction with non-pharmacological strategies and community-based antiviral treatment programmes to reduce the risk that COVID-19 poses to patients with cancer. FUNDING: University of Oxford, University of Southampton, University of Birmingham, Department of Health and Social Care, and Blood Cancer UK.