{"title":"Embracing the Era of Generative AI: Transforming Scientific Publishing in Laboratory Medicine and Pathology","authors":"Amado Tandoc III","doi":"10.21141/pjp.2023.01","DOIUrl":"https://doi.org/10.21141/pjp.2023.01","url":null,"abstract":"","PeriodicalId":166708,"journal":{"name":"Philippine Journal of Pathology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126013911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. M. Legaspi, D. Ong, Jose Iñigo Remulla, R. Agbay
{"title":"SARS-CoV-2 RT-PCR Ct Value and Laboratory Tests: Clinicopathologic Characteristics among Adult Filipino Inpatients diagnosed with COVID-19 in a Tertiary Medical Center","authors":"C. M. Legaspi, D. Ong, Jose Iñigo Remulla, R. Agbay","doi":"10.21141/pjp.2023.07","DOIUrl":"https://doi.org/10.21141/pjp.2023.07","url":null,"abstract":"","PeriodicalId":166708,"journal":{"name":"Philippine Journal of Pathology","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123834861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bien Angelo Kuizon, Karen Damian, Emilio Villanueva III
{"title":"Baseline Complete Blood Count and Cell Population Data as Prognostic Markers for In-Hospital Mortality among COVID-19 Patients admitted at the Philippine General Hospital from March 2020 to January 2022","authors":"Bien Angelo Kuizon, Karen Damian, Emilio Villanueva III","doi":"10.21141/pjp.2023.04","DOIUrl":"https://doi.org/10.21141/pjp.2023.04","url":null,"abstract":"","PeriodicalId":166708,"journal":{"name":"Philippine Journal of Pathology","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114708203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evaluation of the Effectiveness of Lean Six Sigma Approach for SARS-CoV-2 RT-PCR Turnaround Time (TAT) Improvement at a Hospital-Based Tertiary Laboratory","authors":"Dian Lagamayo, R. Agbay, Sarah Jane Datay-Lim","doi":"10.21141/pjp.2023.03","DOIUrl":"https://doi.org/10.21141/pjp.2023.03","url":null,"abstract":"","PeriodicalId":166708,"journal":{"name":"Philippine Journal of Pathology","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124428226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction. Among patients with Acute Myeloid Leukemia (AML), the karyotype at diagnosis is an important prognostic indicator for predicting outcomes. Several studies have been done to identify the most common cytogenetic abnormalities seen in patients in other countries, however, limited studies have been done in our setting. Objective. The study aims to determine the most common abnormalities present among patients with AML referred for Fluorescence in situ Hybridization (FISH) at the National Kidney and Transplant Institute. Methodology. The study included 131 adult patients with a mean age of 46. Fluorescence in situ Hybridization was used to identify the following cytogenetic abnormalities: t(8;21), 11q23 (MLL), 16q22 (CBFB-MYH11), t(15;17) (PML/RARA), t(9;22) (BCR/ABL), 7q31 deletion, and Monosomy 7. Results. FISH was negative in 40% (n=53) of patients. 7q31 deletion is the most frequently identified cytogenetic abnormality among patients with a single abnormality (n=17, 13%) present and is the most frequently identified abnormality among patients with multiple abnormalities (n=26). 7q31 deletion is more frequently observed among patients between the ages 51 to 60 years old and among patients with AML with monocytic differentiation. 22% (n=29) of patients have multiple abnormalities, with the most common abnormalities to occur together are 7q31 deletion and t(8;21) (n=20, 15%). Patients with negative results and patients with multiple cytogenetic abnormalities are commonly seen within the 41 to 50 age group. Conclusion. The current study provides a single-institution view of the cytogenetic abnormalities among adult Filipino patients with AML using FISH. Further investigation on the clinical history of these patients, with correlation with other methods, as well as epidemiologic studies are needed to better understand the similarities and differences seen from previously reported incidences.
{"title":"Profiling of Genetic Mutations among Adult Filipino Patients Diagnosed with Acute Myeloid Leukemia using Fluorescence In Situ Hybridization from 2014 to 2021: A Single-Institution Study","authors":"Aaron Pierre Calimag, Januario Antonio Veloso","doi":"10.21141/pjp.2023.06","DOIUrl":"https://doi.org/10.21141/pjp.2023.06","url":null,"abstract":"Introduction. Among patients with Acute Myeloid Leukemia (AML), the karyotype at diagnosis is an important prognostic indicator for predicting outcomes. Several studies have been done to identify the most common cytogenetic abnormalities seen in patients in other countries, however, limited studies have been done in our setting. Objective. The study aims to determine the most common abnormalities present among patients with AML referred for Fluorescence in situ Hybridization (FISH) at the National Kidney and Transplant Institute. Methodology. The study included 131 adult patients with a mean age of 46. Fluorescence in situ Hybridization was used to identify the following cytogenetic abnormalities: t(8;21), 11q23 (MLL), 16q22 (CBFB-MYH11), t(15;17) (PML/RARA), t(9;22) (BCR/ABL), 7q31 deletion, and Monosomy 7. Results. FISH was negative in 40% (n=53) of patients. 7q31 deletion is the most frequently identified cytogenetic abnormality among patients with a single abnormality (n=17, 13%) present and is the most frequently identified abnormality among patients with multiple abnormalities (n=26). 7q31 deletion is more frequently observed among patients between the ages 51 to 60 years old and among patients with AML with monocytic differentiation. 22% (n=29) of patients have multiple abnormalities, with the most common abnormalities to occur together are 7q31 deletion and t(8;21) (n=20, 15%). Patients with negative results and patients with multiple cytogenetic abnormalities are commonly seen within the 41 to 50 age group. Conclusion. The current study provides a single-institution view of the cytogenetic abnormalities among adult Filipino patients with AML using FISH. Further investigation on the clinical history of these patients, with correlation with other methods, as well as epidemiologic studies are needed to better understand the similarities and differences seen from previously reported incidences.","PeriodicalId":166708,"journal":{"name":"Philippine Journal of Pathology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125868641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C.J. Bernardo, Claire Hemedez, J. Andal, Rubi K. Li, Y. Mascardo, Alizza Mariel Espiritu, Josephine Babida, D. Ang
Introduction. Ovarian cancer is one of the leading causes of mortality in women. In 2020, 5,395 (6.2%) of diagnosed malignancies in females were ovarian in origin. It also ranked second among gynecologic malignancies after cervical cancer. The prevalence in Asian /Pacific women is 9.2 per 100,000 population. Increased mortality and poor prognosis in ovarian cancer are caused by asymptomatic growth and delayed or absent symptoms for which about 70% of women have an advanced stage (III/IV) by the time of diagnosis. The most associated gene mutations are Breast Cancer gene 1 (BRCA1) which is identified in chromosome 17q21 and Breast Cancer gene 2 (BRCA2) identified in chromosome 13. Both proteins function in the double-strand DNA break repair pathway especially in the large framework repair molecules. Olaparib is a first-line drug used in the management of ovarian cancer. It targets affected cells by inhibition of poly (ADP-ribose) polymerase (PARP) activity which induces synthetic lethality in mutated BRCA1/2 cancers by selectively targeting tumor cells that fail to repair DNA double-strand breaks (DSBs). Objectives. The study aims to determine the prevalence of pathogenic somatic mutations in BRCA1 and BRCA2 among patients diagnosed of having ovarian cancer, to characterize the identified variants into benign/ no pathogenic variant identified, variant of uncertain significance (VUS), and pathogenic, and to determine the relationship of specific mutations detected with histomorphologic findings and clinical attributes. Methodology. Ovarian cancer tissues available at the St. Luke’s Medical Center Human Cancer Biobank and formalin-fixed paraffin-embedded (FFPE) tissue blocks diagnosed as ovarian cancer from the year 2016 to 2020 were included. Determination of the prevalence of somatic BRCA1 and BRCA2 mutations using Next Generation Sequencing (NGS). Results. A total of 60 samples were processed, and three samples were excluded from the analysis due to an inadequate number of cells. In the remaining 57 samples diagnosed ovarian tumors, pathogenic BRCA1/2 variants were identified in 10 (17.5%) samples. Among the BRCA1/2 positive samples, 3 (5.3%) BRCA1 and 7 (12.3%) BRCA2 somatic mutations were identified. Conclusion. Identification of specific BRCA1/2 mutations in FFPE samples with NGS plays a big role in the management of ovarian cancer, particularly with the use of targeted therapies such as Olaparib. The use of this drug could provide a longer disease-free survival for these patients. Furthermore, we recommend that women diagnosed with ovarian cancer should be subjected to genetic testing regardless of the histologic subtypes or clinical features. Lastly, genetic testing should be done along with proper genetic counseling, especially for patients who are susceptible to these mutations.
{"title":"Prevalence of Somatic BRCA1 and BRCA2 Mutations in Ovarian Cancer among Filipinos using Next Generation Sequencing","authors":"C.J. Bernardo, Claire Hemedez, J. Andal, Rubi K. Li, Y. Mascardo, Alizza Mariel Espiritu, Josephine Babida, D. Ang","doi":"10.21141/pjp.2023.05","DOIUrl":"https://doi.org/10.21141/pjp.2023.05","url":null,"abstract":"Introduction. Ovarian cancer is one of the leading causes of mortality in women. In 2020, 5,395 (6.2%) of diagnosed malignancies in females were ovarian in origin. It also ranked second among gynecologic malignancies after cervical cancer. The prevalence in Asian /Pacific women is 9.2 per 100,000 population. Increased mortality and poor prognosis in ovarian cancer are caused by asymptomatic growth and delayed or absent symptoms for which about 70% of women have an advanced stage (III/IV) by the time of diagnosis. The most associated gene mutations are Breast Cancer gene 1 (BRCA1) which is identified in chromosome 17q21 and Breast Cancer gene 2 (BRCA2) identified in chromosome 13. Both proteins function in the double-strand DNA break repair pathway especially in the large framework repair molecules. Olaparib is a first-line drug used in the management of ovarian cancer. It targets affected cells by inhibition of poly (ADP-ribose) polymerase (PARP) activity which induces synthetic lethality in mutated BRCA1/2 cancers by selectively targeting tumor cells that fail to repair DNA double-strand breaks (DSBs). Objectives. The study aims to determine the prevalence of pathogenic somatic mutations in BRCA1 and BRCA2 among patients diagnosed of having ovarian cancer, to characterize the identified variants into benign/ no pathogenic variant identified, variant of uncertain significance (VUS), and pathogenic, and to determine the relationship of specific mutations detected with histomorphologic findings and clinical attributes. Methodology. Ovarian cancer tissues available at the St. Luke’s Medical Center Human Cancer Biobank and formalin-fixed paraffin-embedded (FFPE) tissue blocks diagnosed as ovarian cancer from the year 2016 to 2020 were included. Determination of the prevalence of somatic BRCA1 and BRCA2 mutations using Next Generation Sequencing (NGS). Results. A total of 60 samples were processed, and three samples were excluded from the analysis due to an inadequate number of cells. In the remaining 57 samples diagnosed ovarian tumors, pathogenic BRCA1/2 variants were identified in 10 (17.5%) samples. Among the BRCA1/2 positive samples, 3 (5.3%) BRCA1 and 7 (12.3%) BRCA2 somatic mutations were identified. Conclusion. Identification of specific BRCA1/2 mutations in FFPE samples with NGS plays a big role in the management of ovarian cancer, particularly with the use of targeted therapies such as Olaparib. The use of this drug could provide a longer disease-free survival for these patients. Furthermore, we recommend that women diagnosed with ovarian cancer should be subjected to genetic testing regardless of the histologic subtypes or clinical features. Lastly, genetic testing should be done along with proper genetic counseling, especially for patients who are susceptible to these mutations.","PeriodicalId":166708,"journal":{"name":"Philippine Journal of Pathology","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123256371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Enhancing Autopsy Workflow Through a Downdraft Set Up","authors":"M. S. Lenon, S. M. Esposo, A. G. Cabic","doi":"10.21141/pjp.2023.09","DOIUrl":"https://doi.org/10.21141/pjp.2023.09","url":null,"abstract":"","PeriodicalId":166708,"journal":{"name":"Philippine Journal of Pathology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130262080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chris Zielinski, Margaret Winker, Rakesh Aggarwal, Lorraine Ferris, Markus Heinemann, Jose Florencio Lapeña, Sanjay Pai, Edsel Ing, Leslie Citrome, Murad Alam, Michael Voight, F. Habibzadeh
Introduction This statement revises our earlier “WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications” (January 20, 2023). The revision reflects the proliferation of chatbots and their expanding use in scholarly publishing over the last few months, as well as emerging concerns regarding lack of authenticity of content when using chatbots. These Recommendations are intended to inform editors and help them develop policies for the use of chatbots in papers published in their journals. They aim to help authors and reviewers understand how best to attribute the use of chatbots in their work, and to address the need for all journal editors to have access to manuscript screening tools. In this rapidly evolving field, we will continue to modify these recommendations as the software and its applications develop. A chatbot is a tool “[d]riven by [artificial intelligence], automated rules, natural-language processing (NLP), and machine learning (ML)…[to] process data to deliver responses to requests of all kinds.”1 Artificial intelligence (AI) is “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”2 “Generative modeling is an artificial intelligence technique that generates synthetic artifacts by analyzing training examples; learning their patterns and distribution; and then creating realistic facsimiles. Generative AI (GAI) uses generative modeling and advances in deep learning (DL) to produce diverse content at scale by utilizing existing media such as text, graphics, audio, and video.”3, 4 Chatbots are activated by a plain-language instruction, or “prompt,” provided by the user. They generate responses using statistical and probability-based language models.5 This output has some characteristic properties. It is usually linguistically accurate and fluent but, to date, it is often compromised in various ways. For example, chatbot output currently carries the risk of including biases, distortions, irrelevancies, misrepresentations, and plagiarism many of which are caused by the algorithms governing its generation and heavily dependent on the contents of the materials used in its training. Consequently, there are concerns about the effects of chatbots on knowledge creation and dissemination – including their potential to spread and amplify mis- and disinformation6 – and their broader impact on jobs and the economy, as well as the health of individuals and populations. New legal issues have also arisen in connection with chatbots and generative AI.7 Chatbots retain the information supplied to them, including content and prompts, and may use this information in future responses. Therefore, scholarly content that is generated or edited using AI would be retained and as a result, could potentially appear in future responses, further increasing the risk of inadvertent plagiarism on the part of the user and any future users of the tech
{"title":"Chatbots, Generative AI, and Scholarly Manuscripts: WAME Recommendations on Chatbots and Generative Artificial Intelligence in Relation to Scholarly Publications","authors":"Chris Zielinski, Margaret Winker, Rakesh Aggarwal, Lorraine Ferris, Markus Heinemann, Jose Florencio Lapeña, Sanjay Pai, Edsel Ing, Leslie Citrome, Murad Alam, Michael Voight, F. Habibzadeh","doi":"10.21141/pjp.2023.08","DOIUrl":"https://doi.org/10.21141/pjp.2023.08","url":null,"abstract":"Introduction \u0000This statement revises our earlier “WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications” (January 20, 2023). The revision reflects the proliferation of chatbots and their expanding use in scholarly publishing over the last few months, as well as emerging concerns regarding lack of authenticity of content when using chatbots. These Recommendations are intended to inform editors and help them develop policies for the use of chatbots in papers published in their journals. They aim to help authors and reviewers understand how best to attribute the use of chatbots in their work, and to address the need for all journal editors to have access to manuscript screening tools. In this rapidly evolving field, we will continue to modify these recommendations as the software and its applications develop. \u0000 A chatbot is a tool “[d]riven by [artificial intelligence], automated rules, natural-language processing (NLP), and machine learning (ML)…[to] process data to deliver responses to requests of all kinds.”1 Artificial intelligence (AI) is “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”2 \u0000 “Generative modeling is an artificial intelligence technique that generates synthetic artifacts by analyzing training examples; learning their patterns and distribution; and then creating realistic facsimiles. Generative AI (GAI) uses generative modeling and advances in deep learning (DL) to produce diverse content at scale by utilizing existing media such as text, graphics, audio, and video.”3, 4 \u0000 Chatbots are activated by a plain-language instruction, or “prompt,” provided by the user. They generate responses using statistical and probability-based language models.5 This output has some characteristic properties. It is usually linguistically accurate and fluent but, to date, it is often compromised in various ways. For example, chatbot output currently carries the risk of including biases, distortions, irrelevancies, misrepresentations, and plagiarism many of which are caused by the algorithms governing its generation and heavily dependent on the contents of the materials used in its training. Consequently, there are concerns about the effects of chatbots on knowledge creation and dissemination – including their potential to spread and amplify mis- and disinformation6 – and their broader impact on jobs and the economy, as well as the health of individuals and populations. New legal issues have also arisen in connection with chatbots and generative AI.7 \u0000 Chatbots retain the information supplied to them, including content and prompts, and may use this information in future responses. Therefore, scholarly content that is generated or edited using AI would be retained and as a result, could potentially appear in future responses, further increasing the risk of inadvertent plagiarism on the part of the user and any future users of the tech","PeriodicalId":166708,"journal":{"name":"Philippine Journal of Pathology","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125280728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hyacinth Joy Balderama, V. Tesoro, T. Antonio, R. Ramones
{"title":"Institutionalization of the Philippine Health Laboratory System (PHLS)","authors":"Hyacinth Joy Balderama, V. Tesoro, T. Antonio, R. Ramones","doi":"10.21141/pjp.2022.20","DOIUrl":"https://doi.org/10.21141/pjp.2022.20","url":null,"abstract":"","PeriodicalId":166708,"journal":{"name":"Philippine Journal of Pathology","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115668609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Five Hundred Twenty Five Thousand Six Hundred Minutes","authors":"A. Tandoc","doi":"10.21141/pjp.2022.19","DOIUrl":"https://doi.org/10.21141/pjp.2022.19","url":null,"abstract":"","PeriodicalId":166708,"journal":{"name":"Philippine Journal of Pathology","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124395972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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