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Article: Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review.

Wang, Hsin-Yao / Lin, Wan-Ying / Zhou, Chenfei / Yang, Zih-Ang / Kalpana, Sriram / Lebowitz, Michael S

2024 Volume 16, Issue 5

Abstract: The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a ... ...

Abstract	The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.
Language	English
Publishing date	2024-02-21
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers16050862
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Alternate Antimicrobial Therapies and Their Companion Tests.

Kalpana, Sriram / Lin, Wan-Ying / Wang, Yu-Chiang / Fu, Yiwen / Wang, Hsin-Yao

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 15

Abstract: New antimicrobial approaches are essential to counter antimicrobial resistance. The drug development pipeline is exhausted with the emergence of resistance, resulting in unsuccessful trials. The lack of an effective drug developed from the conventional ... ...

Abstract	New antimicrobial approaches are essential to counter antimicrobial resistance. The drug development pipeline is exhausted with the emergence of resistance, resulting in unsuccessful trials. The lack of an effective drug developed from the conventional drug portfolio has mandated the introspection into the list of potentially effective unconventional alternate antimicrobial molecules. Alternate therapies with clinically explicable forms include monoclonal antibodies, antimicrobial peptides, aptamers, and phages. Clinical diagnostics optimize the drug delivery. In the era of diagnostic-based applications, it is logical to draw diagnostic-based treatment for infectious diseases. Selection criteria of alternate therapeutics in infectious diseases include detection, monitoring of response, and resistance mechanism identification. Integrating these diagnostic applications is disruptive to the traditional therapeutic development. The challenges and mitigation methods need to be noted. Applying the goals of clinical pharmacokinetics that include enhancing efficacy and decreasing toxicity of drug therapy, this review analyses the strong correlation of alternate antimicrobial therapeutics in infectious diseases. The relationship between drug concentration and the resulting effect defined by the pharmacodynamic parameters are also analyzed. This review analyzes the perspectives of aligning diagnostic initiatives with the use of alternate therapeutics, with a particular focus on companion diagnostic applications in infectious diseases.
Language	English
Publishing date	2023-07-26
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13152490
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Article: Evaluation of Platelet Alloimmunization by Filtration Enzyme-Linked Immunosorbent Assay.

Chiueh, Tzong-Shi / Wang, Hsin-Yao / Wu, Min-Hsien / Hsueh, Yu-Shan / Chen, Hui-Chu

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 10

Abstract: The current methods for detecting antiplatelet antibodies are mostly manual and labor-intensive. A convenient and rapid detection method is required for effectively detecting alloimmunization during platelet transfusion. In our study, to detect ... ...

Abstract	The current methods for detecting antiplatelet antibodies are mostly manual and labor-intensive. A convenient and rapid detection method is required for effectively detecting alloimmunization during platelet transfusion. In our study, to detect antiplatelet antibodies, positive and negative sera of random-donor antiplatelet antibodies were collected after completing a routine solid-phase red cell adherence test (SPRCA). Platelet concentrates from our random volunteer donors were also prepared using the ZZAP method and then used in a faster, significantly less labor-intensive process, a filtration enzyme-linked immunosorbent assay (fELISA), for detecting antibodies against platelet surface antigens. All fELISA chromogen intensities were processed using ImageJ software. By dividing the final chromogen intensity of each test serum with the background chromogen intensity of whole platelets, the reactivity ratios of fELISA can be used to differentiate positive SPRCA sera from negative sera. A sensitivity of 93.9% and a specificity of 93.3% were obtained for 50 μL of sera using fELISA. The area under the ROC curve reached 0.96 when comparing fELISA with the SPRCA test. We have successfully developed a rapid fELISA method for detecting antiplatelet antibodies.
Language	English
Publishing date	2023-05-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13101704
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Article: Antibiotic Resistance Diagnosis in ESKAPE Pathogens-A Review on Proteomic Perspective.

Kalpana, Sriram / Lin, Wan-Ying / Wang, Yu-Chiang / Fu, Yiwen / Lakshmi, Amrutha / Wang, Hsin-Yao

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 6

Abstract: Antibiotic resistance has emerged as an imminent pandemic. Rapid diagnostic assays distinguish bacterial infections from other diseases and aid antimicrobial stewardship, therapy optimization, and epidemiological surveillance. Traditional methods ... ...

Abstract	Antibiotic resistance has emerged as an imminent pandemic. Rapid diagnostic assays distinguish bacterial infections from other diseases and aid antimicrobial stewardship, therapy optimization, and epidemiological surveillance. Traditional methods typically have longer turn-around times for definitive results. On the other hand, proteomic studies have progressed constantly and improved both in qualitative and quantitative analysis. With a wide range of data sets made available in the public domain, the ability to interpret the data has considerably reduced the error rates. This review gives an insight on state-of-the-art proteomic techniques in diagnosing antibiotic resistance in ESKAPE pathogens with a future outlook for evading the "imminent pandemic".
Language	English
Publishing date	2023-03-07
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13061014
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Article ; Online: Data-Driven Two-Stage Framework for Identification and Characterization of Different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry Data.

Chung, Chia-Ru / Wang, Hsin-Yao / Yao, Chun-Han / Wu, Li-Ching / Lu, Jang-Jih / Horng, Jorng-Tzong / Lee, Tzong-Yi

Microbiology spectrum

2023 Volume 11, Issue 3, Page(s) e0347922

Abstract: In clinical microbiology, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in ... ...

Abstract	In clinical microbiology, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in Escherichia coli based on a large amount of MALDI-TOF MS data has not yet been reported. This may be because building a prediction model to cover all E. coli isolates would be challenging given the high diversity of the E. coli population. This study aimed to develop a MALDI-TOF MS-based, data-driven, two-stage framework for characterizing different AMRs in E. coli. Specifically, amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM) were used. In the first stage, we split the data into two groups based on informative peaks according to the importance of the random forest. In the second stage, prediction models were constructed using four different machine learning algorithms-logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The findings demonstrate that XGBoost outperformed the other four machine learning models. The values of the area under the receiver operating characteristic curve were 0.62, 0.72, 0.87, 0.72, and 0.72 for AMC, CAZ, CIP, CRO, and CXM, respectively. This implies that a data-driven, two-stage framework could improve accuracy by approximately 2.8%. As a result, we developed AMR prediction models for E. coli using a data-driven two-stage framework, which is promising for assisting physicians in making decisions. Further, the analysis of informative peaks in future studies could potentially reveal new insights.
MeSH term(s)	Escherichia coli ; Anti-Bacterial Agents/pharmacology ; Ceftriaxone/pharmacology ; Ceftazidime ; Cefuroxime ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods ; Ciprofloxacin ; Amoxicillin
Chemical Substances	Anti-Bacterial Agents ; Ceftriaxone (75J73V1629) ; Ceftazidime (9M416Z9QNR) ; Cefuroxime (O1R9FJ93ED) ; Ciprofloxacin (5E8K9I0O4U) ; Amoxicillin (804826J2HU)
Language	English
Publishing date	2023-04-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2807133-5
ISSN	2165-0497 ; 2165-0497
ISSN (online)	2165-0497
ISSN	2165-0497
DOI	10.1128/spectrum.03479-22
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Article ; Online: Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra.

Chung, Chia-Ru / Wang, Hsin-Yao / Chou, Po-Han / Wu, Li-Ching / Lu, Jang-Jih / Horng, Jorng-Tzong / Lee, Tzong-Yi

International journal of molecular sciences

2023 Volume 24, Issue 2

Abstract: Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical ... ...

Abstract	Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical information from complicated MS spectral data. Different preprocessing methods yield different data, and the optimal approach is unclear. In this study, we adopted an ensemble of multiple preprocessing methods--FlexAnalysis, MALDIquant, and continuous wavelet transform-based methods--to detect peaks and build machine learning classifiers, including logistic regressions, naïve Bayes classifiers, random forests, and a support vector machine. The aim was to identify antibiotic resistance in
MeSH term(s)	Humans ; Anti-Bacterial Agents/pharmacology ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods ; Bayes Theorem ; Acinetobacter baumannii/chemistry ; Acinetobacter Infections
Chemical Substances	Anti-Bacterial Agents
Language	English
Publishing date	2023-01-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms24020998
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Article ; Online: Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection.

Chen, Li-Chin / Hung, Kuo-Hsuan / Tseng, Yi-Ju / Wang, Hsin-Yao / Lu, Tse-Min / Huang, Wei-Chieh / Tsao, Yu

IEEE journal of translational engineering in health and medicine

2023 Volume 12, Page(s) 43–55

Abstract: Objective: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, the inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal ... ...

Abstract	Objective: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, the inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. Methods and procedures: GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for target vessel revascularization (TVR) detection. Results: The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority ([Formula: see text]) compared to prior GLP processing. Conclusion: Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise. Clinical impact: Our study effectively transposes patient progression from one cohort to another, surpassing the constraints of episodic observation. The transferability of disease progression contributed to cardiovascular event assessment.
MeSH term(s)	Humans ; Absenteeism ; Benchmarking ; Cardiovascular Diseases/diagnosis ; Disease Progression ; Supervised Machine Learning
Language	English
Publishing date	2023-08-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	2696555-0
ISSN	2168-2372 ; 2168-2372
ISSN (online)	2168-2372
ISSN	2168-2372
DOI	10.1109/JTEHM.2023.3307794
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Article ; Online: Trend of HPV Molecular Epidemiology in the Post-Vaccine Era: A 10-Year Study.

Lin, Yueh / Lin, Wan-Ying / Lin, Ting-Wei / Tseng, Yi-Ju / Wang, Yu-Chiang / Yu, Jia-Ruei / Chung, Chia-Ru / Wang, Hsin-Yao

Viruses

2023 Volume 15, Issue 10

Abstract: Cervical cancer, a major health concern among women worldwide, is closely linked to human papillomavirus (HPV) infection. This study explores the evolving landscape of HPV molecular epidemiology in Taiwan over a decade (2010-2020), where prophylactic HPV ...

Abstract	Cervical cancer, a major health concern among women worldwide, is closely linked to human papillomavirus (HPV) infection. This study explores the evolving landscape of HPV molecular epidemiology in Taiwan over a decade (2010-2020), where prophylactic HPV vaccination has been implemented since 2007. Analyzing data from 40,561 vaginal swab samples, with 42.0% testing positive for HPV, we reveal shifting trends in HPV genotype distribution and infection patterns. The 12 high-risk genotypes, in order of decreasing percentage, were HPV 52, 58, 16, 18, 51, 56, 39, 59, 33, 31, 45, and 35. The predominant genotypes were HPV 52, 58, and 16, accounting for over 70% of cases annually. The proportions of high-risk and non-high-risk HPV infections varied across age groups. High-risk infections predominated in sexually active individuals aged 30-50 and were mixed-type infections. The composition of high-risk HPV genotypes was generally stable over time; however, HPV31, 33, 39, and 51 significantly decreased over the decade. Of the strains, HPV31 and 33 are shielded by the nonavalent HPV vaccine. However, no reduction was noted for the other seven genotypes. This study offers valuable insights into the post-vaccine HPV epidemiology. Future investigations should delve into HPV vaccines' effects and their implications for cervical cancer prevention strategies. These findings underscore the need for continued surveillance and research to guide effective public health interventions targeting HPV-associated diseases.
MeSH term(s)	Humans ; Female ; Uterine Cervical Neoplasms/epidemiology ; Uterine Cervical Neoplasms/prevention & control ; Human Papillomavirus Viruses ; Papillomavirus Infections/epidemiology ; Papillomavirus Infections/prevention & control ; Molecular Epidemiology ; Papillomavirus Vaccines ; Papillomaviridae/genetics ; Genotype ; Human papillomavirus 31/genetics ; Prevalence
Chemical Substances	Papillomavirus Vaccines
Language	English
Publishing date	2023-09-27
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2516098-9
ISSN	1999-4915 ; 1999-4915
ISSN (online)	1999-4915
ISSN	1999-4915
DOI	10.3390/v15102015
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Risk Stratification for Herpes Simplex Virus Pneumonia Using Elastic Net Penalized Cox Proportional Hazard Algorithm with Enhanced Explainability.

Wang, Yu-Chiang / Lin, Wan-Ying / Tseng, Yi-Ju / Fu, Yiwen / Li, Weijia / Huang, Yu-Chen / Wang, Hsin-Yao

Journal of clinical medicine

2023 Volume 12, Issue 13

Abstract: Herpes simplex virus (HSV) pneumonia is a serious and often fatal respiratory tract infection that occurs in immunocompromised individuals. The early detection of accurate risk stratification is essential in identifying patients who are at high risk of ... ...

Abstract	Herpes simplex virus (HSV) pneumonia is a serious and often fatal respiratory tract infection that occurs in immunocompromised individuals. The early detection of accurate risk stratification is essential in identifying patients who are at high risk of mortality and may benefit from more aggressive treatment. In this study, we developed and validated a risk stratification model for HSV bronchopneumonia using an elastic net penalized Cox proportional hazard algorithm. We analyzed data from a cohort of 104 critically ill patients with HSV bronchopneumonia identified in Chang Gung Memorial Hospital, Linkou, Taiwan: one of the largest tertiary medical centers in the world. A total of 109 predictors, both clinical and laboratory, were identified in this process to develop a risk stratification model that could accurately predict mortality in patients with HSV bronchopneumonia. This model was able to differentiate the risk of death and predict mortality in patients with HSV bronchopneumonia compared to the APACHE II score in the early stage of ICU admissions. Both hazard ratio coefficient and selection frequency were used as the metrics to enhance the explainability of the informative predictors. Our findings suggest that the elastic net penalized Cox proportional hazard algorithm is a promising tool for risk stratification in patients with HSV bronchopneumonia and could be useful in identifying those at high risk of mortality.
Language	English
Publishing date	2023-07-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662592-1
ISSN	2077-0383
ISSN	2077-0383
DOI	10.3390/jcm12134489
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Article: Rapid Antibiotic Resistance Serial Prediction in

Zhang, Jiahong / Wang, Zhuo / Wang, Hsin-Yao / Chung, Chia-Ru / Horng, Jorng-Tzong / Lu, Jang-Jih / Lee, Tzong-Yi

Frontiers in microbiology

2022 Volume 13, Page(s) 853775

Abstract: Multidrug resistance has become a phenotype that commonly exists ... ...

Abstract	Multidrug resistance has become a phenotype that commonly exists among
Language	English
Publishing date	2022-04-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2587354-4
ISSN	1664-302X
ISSN	1664-302X
DOI	10.3389/fmicb.2022.853775
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