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2. Coping Skills Mobile App to Support the Emotional Well-Being of Young People During the COVID-19 Pandemic: Protocol for a Mixed Methods Study

Author

Serlachius, Anna, Schache, Kiralee, Boggiss, Anna, Lim, David, Wallace-Boyd, Kate, Brenton-Peters, Jennifer, Buttenshaw, Elise, Chadd, Stephanie, Cavadino, Alana, Cao, Nicholas, Morunga, Eva, and Thabrew, Hiran

Subjects
Medicine, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundThe COVID-19 pandemic is likely to increase anxiety and distress in young people worldwide. It is important to prioritize mental health during crisis events to mitigate the negative and often long-term effects of the crises on young people, families, and society. Mental health and well-being apps represent a scalable approach for improving psychological outcomes in young people and have potential to improve the equity of service access. ObjectiveThe Whitu: 7 Ways in 7 Days well-being app was recently developed by our group to address the urgent need for innovative approaches to reach young New Zealanders who are struggling to cope with the COVID-19 pandemic. The aim of this study is twofold: to evaluate the acceptability of the prototype app and to examine the effectiveness of the refined app at improving mental and emotional well-being and reducing depression, anxiety, and stress in young people in New Zealand. MethodsA two-phase mixed methods study will be undertaken to achieve these aims. During the first phase, 20 young people aged 16-30 years (including those of Māori and Pacific ethnicity) will participate in a qualitative study to help refine the prototype app. During the second phase, 90 young people aged 16-30 years will participate in a randomized waitlist-controlled trial (RCT) to evaluate the efficacy of the refined Whitu app at 4 weeks and 3 months after baseline. Outcomes will be evaluated using validated web-based questionnaires at baseline, 4 weeks, and 3 months. ResultsThe study received ethics approval in May 2020, and recruitment for the focus groups commenced in June 2020. Recruitment for the RCT is expected to commence in October 2020. Participants for both study phases will be recruited via social media and web-based communities. Data collection for the RCT is expected to be completed by January 2021, and analyses are expected to be completed by March 2021. Linear mixed modelling will be used to determine between-group differences in psychological outcomes. ConclusionsThere is an urgent need to develop culturally appropriate, scalable mental health interventions to address the psychological consequences of the COVID-19 pandemic. In this study, we will develop and test an evidence-based well-being app that, if effective, can be made available to all young people in New Zealand and internationally. Trial RegistrationAustralian New Zealand Clinical Trials Registry (ACTRN12620000516987); https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=379597. International Registered Report Identifier (IRRID)PRR1-10.2196/23716

Published
2020
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5. Prevention of persistent pain with lidocaine infusions in breast cancer surgery (PLAN): study protocol for a multicenter randomized controlled trial

Author

Khan, James S., Gilron, Ian, Devereaux, P. J., Clarke, Hance, Ayach, Nour, Tomlinson, George, Quan, May Lynn, Ladha, Karim S., Choi, Stephen, Munro, Allana, Brull, Richard, Lim, David W., Avramescu, Sinziana, Richebé, Philippe, Hodgson, Nicole, Paul, James, McIsaac, Daniel I., Derzi, Simone, Zbitnew, Geoff L., Easson, Alexandra M., Siddiqui, Naveed T., Miles, Sarah J., and Karkouti, Keyvan

Published
2024
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6. Coherently complete algebraic stacks in positive characteristic

Author

Alper, Jarod, Hall, Jack, and Lim, David Benjamin

Subjects
Mathematics - Algebraic Geometry, Primary 14F06, 14A20, Secondary 14F08
Abstract

With the long-term goal of proving local structure theorems of algebraic stacks in positive characteristic near points with reductive (but possibly non-linearly reductive) stabilizer, we conjecture that quotient stacks of the form $[\mathrm{Spec}\, A/G]$, with $G$ reductive and $A^G$ complete local, are coherently complete along the unique closed point. We establish this conjecture in two interesting cases: (1) $A^G$ is artinian and (2) $G$ acts trivially on $\mathrm{Spec}\, A$. We also establish coherent completeness results for graded unipotent group actions. In order to establish these results, we prove a number of foundational statements concerning cohomological and completeness properties of algebraic stacks -- including on how these properties ascend and descend along morphisms., Comment: 35 pages, comments welcome!

Published
2023

7. Deeply-Learned Generalized Linear Models with Missing Data

Author

Lim, David K, Rashid, Naim U, Oliva, Junier B, and Ibrahim, Joseph G

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Computation, Statistics - Methodology
Abstract

Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to supervised learning problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in modern biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of deeply learned generalized linear models, a supervised DL architecture for regression and classification problems. We propose a new architecture, \textit{dlglm}, that is one of the first to be able to flexibly account for both ignorable and non-ignorable patterns of missingness in input features and response at training time. We demonstrate through statistical simulation that our method outperforms existing approaches for supervised learning tasks in the presence of missing not at random (MNAR) missingness. We conclude with a case study of a Bank Marketing dataset from the UCI Machine Learning Repository, in which we predict whether clients subscribed to a product based on phone survey data. Supplementary materials for this article are available online.

Published
2022
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12. Grounding, Framing, and Problematising Research in Online, Distance and Digital Education

Author

Lim, David C. L., Zawacki-Richter, Olaf, and Jung, Insung

Abstract

Purpose: This paper engages Olaf Zawacki-Richter and Insung Jung in a frank and penetrating conversation that seeks to ground, frame, and problematise research in the field conceptualised as "open, distance and digital education" (ODDE). Taking as starting point the recent publication of the landmark "Handbook of Open, Distance, and Digital Education" (2022), it segues into a broad critique of the shortcomings of ODDE research, the importance of knowledge production on the meso- and macro-levels, the autonomy of ODDE as a field coming into its own, the place of postfoundationalism in ODDE discourse, and related topics that are pivotal in ODDE today. Design/methodology/approach: The semi-structured interview was employed as the primary qualitative research method. Findings: The research imperative of the relatively young but complex field of ODDE today is not the incessant reiteration of the same but rather a strategic reorientation that, first, circumvents the well-documented yet too-often-overlooked shortcomings of ODDE research and, second, promotes transboundary collaborations with the potential for system-wide impact. Originality/value: This novel interview-based critique of ODDE research demonstrates that extending the scholarly discourse beyond the conventional report format is a productive method for enriching conversations on ODDE and vitalising the field itself.

Published
2023
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13. Bridging the gap between prostate radiology and pathology through machine learning

Author

Bhattacharya, Indrani, Lim, David S., Aung, Han Lin, Liu, Xingchen, Seetharaman, Arun, Kunder, Christian A., Shao, Wei, Soerensen, Simon J. C., Fan, Richard E., Ghanouni, Pejman, To'o, Katherine J., Brooks, James D., Sonn, Geoffrey A., and Rusu, Mirabela

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Prostate cancer is the second deadliest cancer for American men. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosis, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements. Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model training. In this study, we compare different labeling strategies, namely, pathology-confirmed radiologist labels, pathologist labels on whole-mount histopathology images, and lesion-level and pixel-level digital pathologist labels (previously validated deep learning algorithm on histopathology images to predict pixel-level Gleason patterns) on whole-mount histopathology images. We analyse the effects these labels have on the performance of the trained machine learning models. Our experiments show that (1) radiologist labels and models trained with them can miss cancers, or underestimate cancer extent, (2) digital pathologist labels and models trained with them have high concordance with pathologist labels, and (3) models trained with digital pathologist labels achieve the best performance in prostate cancer detection in two different cohorts with different disease distributions, irrespective of the model architecture used. Digital pathologist labels can reduce challenges associated with human annotations, including labor, time, inter- and intra-reader variability, and can help bridge the gap between prostate radiology and pathology by enabling the training of reliable machine learning models to detect and localize prostate cancer on MRI., Comment: Indrani Bhattacharya and David S. Lim contributed equally as first authors. Geoffrey A. Sonn and Mirabela Rusu contributed equally as senior authors

Published
2021
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14. Global pattern, trend, and cross-country inequality of early musculoskeletal disorders from 1990 to 2019, with projection from 2020 to 2050

Author

Jin, Yingzhao, Guo, Cui, Abbasian, Mohammadreza, Abbasifard, Mitra, Abbott, J. Haxby, Abdullahi, Auwal, Abedi, Aidin, Abidi, Hassan, Abolhassani, Hassan, Abu-Gharbieh, Eman, Aburuz, Salahdein, Abu-Zaid, Ahmed, Addo, Isaac Yeboah, Adegboye, Oyelola A., Adepoju, Abiola Victor, Adikusuma, Wirawan, Adnani, Qorinah Estiningtyas Sakilah, Aghamiri, Shahin, Ahmad, Danish, Ahmed, Ayman, Aithala, Janardhana P., Akhlaghi, Shiva, Akkala, Sreelatha, Alalwan, Tariq A., Albashtawy, Mohammed, Alemi, Hediyeh, Alhalaiqa, Fadwa Alhalaiqa Naji, Ali, Endale Alemayehu, Almustanyir, Sami, Al-Raddadi, Rajaa M., Alvis-Zakzuk, Nelson J., Al-Worafi, Yaser Mohammed, Alzahrani, Hosam, Alzoubi, Karem H., Amiri, Sohrab, Amu, Hubert, Amzat, Jimoh, Anderson, David B., Anil, Abhishek, Antony, Benny, Arabloo, Jalal, Areda, Damelash, Artaman, Al, Artamonov, Anton A., Aryal, Krishna K., Asghari-Jafarabadi, Mohammad, Ashraf, Tahira, Athari, Seyyed Shamsadin, Atinafu, Bantalem Tilaye, Atout, Maha Moh’d Wahbi, Azadnajafabad, Sina, Azhdari Tehrani, Hamed, Azzam, Ahmed Y., Badawi, Alaa, Baghcheghi, Nayereh, Bai, Ruhai, Baigi, Vali, Banach, Maciej, Banakar, Morteza, Banik, Biswajit, Bardhan, Mainak, Bärnighausen, Till Winfried, Barqawi, Hiba Jawdat, Barrow, Amadou, Bashiri, Azadeh, Batra, Kavita, Bayani, Mojtaba, Bayileyegn, Nebiyou Simegnew, Begde, Ahmet, Beyene, Kebede A., Bhagavathula, Akshaya Srikanth, Bhardwaj, Pankaj, Bhatti, Gurjit Kaur, Bhatti, Jasvinder Singh, Bhatti, Rajbir, Bijani, Ali, Bitra, Veera R., Brazo-Sayavera, Javier, Buchbinder, Rachelle, Burkart, Katrin, Bustanji, Yasser, Butt, Muhammad Hammad, Cámera, Luis Alberto, Carvalho, Felix, Chattu, Vijay Kumar, Chaurasia, Akhilanand, Chen, Guangjin, Chen, Haowei, Chen, Lingxiao, Christensen, Steffan Wittrup McPhee, Chu, Dinh-Toi, Chukwu, Isaac Sunday, Comachio, Josielli, Cruz-Martins, Natália, Cuschieri, Sarah, Dadana, Sriharsha, Dadras, Omid, Dai, Xiaochen, Dai, Zhaoli, Das, Saswati, Dashti, Mohsen, Delgado-Enciso, Ivan, Demisse, Biniyam, Denova-Gutiérrez, Edgar, Desye, Belay, Dewan, Syed Masudur Rahman, Dhingra, Sameer, Diress, Mengistie, Do, Thanh Chi, Do, Thao Huynh Phuong, Doan, Khanh Duy Khanh, Dutta, Sulagna, Dziedzic, Arkadiusz Marian, Edinur, Hisham Atan, Ekholuenetale, Michael, Elhadi, Muhammed, Eskandarieh, Sharareh, Esposito, Francesco, Fagbamigbe, Adeniyi Francis, Farokh, Parisa, Fatehizadeh, Ali, Feizkhah, Alireza, Fekadu, Ginenus, Ferreira, Nuno, Fetensa, Getahun, Fischer, Florian, Foroutan, Behzad, Foroutan Koudehi, Masoumeh, Franklin, Richard Charles, Fukumoto, Takeshi, Gandhi, Aravind P., Ganesan, Balasankar, Gau, Shuo-Yan, Gautam, Rupesh K., Gebre, Abadi Kahsu, Gebregergis, Miglas W.W., Ghaderi Yazdi, Bardiya, Gholami, Ali, Gill, Tiffany K., Goleij, Pouya, Gomes-Neto, Mansueto, Goyal, Anmol, Graham, Simon Matthew, Guan, Bin, Gupta, Bhawna, Gupta, Indarchand Ratanlal, Gupta, Sapna, Gupta, Veer Bala, Gupta, Vivek Kumar, Habibzadeh, Farrokh, Hailu, Wase Benti, Hajibeygi, Ramtin, Halwani, Rabih, Haro, Josep Maria, Hartvigsen, Jan, Hasaballah, Ahmed I., Haubold, Johannes, Hebert, Jeffrey J., Hegazy, Mohamed I., Heidari, Golnaz, Heidari, Mohammad, Hezam, Kamal, Hiraike, Yuta, Hosseinzadeh, Hassan, Hosseinzadeh, Mehdi, Hoveidaei, Amir Human, Hsu, Chi-Jen, Huda, Md Nazmul, Huynh, Hong-Han, Hwang, Bing-Fang, Ibitoye, Segun Emmanuel, Ikiroma, Adalia I., Ilic, Irena M., Ilic, Milena D., Iranmehr, Arad, Islam, Sheikh Mohammed Shariful, Ismail, Nahlah Elkudssiah, Iso, Hiroyasu, Iwagami, Masao, Iyasu, Assefa N., Jacob, Louis, Jafarzadeh, Abdollah, Jahankhani, Kasra, Jain, Nityanand, Jairoun, Ammar Abdulrahman, Janakiraman, Balamurugan, Jayarajah, Umesh, Jayaram, Shubha, Jeganathan, Jayakumar, Jokar, Mohammad, Jonas, Jost B., Joo, Tamas, Joseph, Nitin, Joshua, Charity Ehimwenma, Kabito, Gebisa Guyasa, Kamal, Vineet Kumar, Kandel, Himal, Kantar, Rami S., Karami, Jafar, Karaye, Ibraheem M., Karimi Behnagh, Arman, Kaur, Navjot, Kazemi, Foad, Kedir, Shemsu, Khadembashiri, Mohamad Mehdi, Khadembashiri, Mohammad Amin, Khader, Yousef Saleh, Khajuria, Himanshu, Khan, Mohammad Jobair, Khan, Moien AB, Khan Suheb, Mahammed Ziauddin, Khatatbeh, Haitham, Khatatbeh, Moawiah Mohammad, Khateri, Sorour, Khayat Kashani, Hamid Reza, Khonji, Mohammad Saeid, Khubchandani, Jagdish, Kian, Saeid, Kisa, Adnan, Kitila, Aiggan Tamene, Kolahi, Ali-Asghar, Koohestani, Hamid Reza, Korzh, Oleksii, Kostev, Karel, Kotnis, Ashwin Laxmikant, Koyanagi, Ai, Krishan, Kewal, Kuddus, Mohammed, Kumar, Narinder, Kurniasari, Maria Dyah, Ladan, Muhammad Awwal, Lahariya, Chandrakant, Laksono, Tri, Lallukka, Tea, Landires, Iván, Lasrado, Savita, Lawal, Basira Kankia, Le, Thao Thi Thu, Le, Trang Diep Thanh, Lee, Munjae, Lee, Wei-Chen, Lee, Yo Han, Lerango, Temesgen L., Lim, David, Lim, Stephen S., Lucchetti, Giancarlo, Ma, Zheng Feei, Maghazachi, Azzam A., Maghbouli, Nastaran, Malakan Rad, Elaheh, Malhotra, Armaan, Malik, Ahmad Azam, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Manu, Emmanuel, Mathangasinghe, Yasith, Mazzotti, Antonio, McPhail, Steven M., Mengist, Belayneh, Mesregah, Mohamed Kamal, Mestrovic, Tomislav, Miller, Ted R., Minh, Le Huu Nhat, Mirahmadi Eraghi, Mohammad, Mirrakhimov, Erkin M., Misganaw, Awoke, Mohamadian, Hashem, Mohamadkhani, Ashraf, Mohamed, Nouh Saad, Mohammadi, Esmaeil, Mohammadi, Soheil, Mohammed, Mesud, Mojiri-Forushani, Hoda, Mokdad, Ali H., Momenzadeh, Kaveh, Momtazmanesh, Sara, Monasta, Lorenzo, Montazeri, Fateme, Moradi, Yousef, Morrison, Shane Douglas, Mostafavi, Ebrahim, Mousavi, Parsa, Mousavi, Seyed Ehsan, Mulita, Admir, Murillo-Zamora, Efrén, Mustafa, Ghulam, Muthu, Sathish, Naik, Ganesh R., Naimzada, Mukhammad David, Nakhostin Ansari, Noureddin, Narasimha Swamy, Sreenivas, Nargus, Shumaila, Nascimento, Paulo R.C., Naseri, Amirreza, Natto, Zuhair S., Naveed, Muhammad, Nayak, Biswa Prakash, Nazri-Panjaki, Athare, Negaresh, Mohammad, Negash, Hadush, Nejadghaderi, Seyed Aria, Nguyen, Dang H., Nguyen, Hau Thi Hien, Nguyen, Hien Quang, Nguyen, Phat Tuan, Nguyen, Van Thanh, Niazi, Robina Khan, Ofakunrin, Akinyemi O.D., Okati-Aliabad, Hassan, Okonji, Osaretin Christabel, Olatubi, Matthew Idowu, Ommati, Mohammad Mehdi, Ordak, Michal, Owolabi, Mayowa O., P A, Mahesh, Padubidri, Jagadish Rao, Pan, Feng, Pantazopoulos, Ioannis, Park, Seoyeon, Patel, Jay, Patil, Shankargouda, Pawar, Shrikant, Pedersini, Paolo, Peprah, Prince, Perna, Simone, Petcu, Ionela-Roxana, Petermann-Rocha, Fanny Emily, Pham, Hoang Tran, Pigeolet, Manon, Prates, Elton Junio Sady, Rahim, Fakher, Rahimi, Zahra, Rahimi-Dehgolan, Shahram, Rahimi-Movaghar, Vafa, Rahman, Mohammad Hifz Ur, Rahmati, Masoud, Ramasamy, Shakthi Kumaran, Ramasubramani, Premkumar, Rapaka, Deepthi, Rashedi, Sina, Rashedi, Vahid, Rashidi, Mohammad-Mahdi, Rasouli-Saravani, Ashkan, Rawaf, Salman, Reddy, Murali Mohan Rama Krishna, Redwan, Elrashdy Moustafa Mohamed, Rezaei, Nazila, Rezaei, Negar, Rezaei, Nima, Rezaei, Zahed, Riad, Abanoub, Roever, Leonardo, Roshanzamir, Sharareh, Roy, Priyanka, de Andrade Ruela, Guilherme, Saad, Aly M.A., Saddik, Basema, Sadeghian, Farideh, Saeed, Umar, Safary, Azam, Saghazadeh, Amene, Sagoe, Dominic, Sharif-Askari, Fatemeh Saheb, Sharif-Askari, Narjes Saheb, Sahebkar, Amirhossein, Sakshaug, Joseph W., Salami, Afeez Abolarinwa, Saleh, Mohamed A., Salehi, Sana, Samadzadeh, Sara, Samodra, Yoseph Leonardo, Samuel, Vijaya Paul, Santos, Djanilson B., Santric-Milicevic, Milena M., Saqib, Muhammad Arif Nadeem, Saravanan, Aswini, Sawyer, Susan, Schaarschmidt, Benedikt Michael, Senapati, Sabyasachi, Sethi, Yashendra, Seylani, Allen, Shafaat, Amir, Shafie, Mahan, Shahabi, Saeed, Shahbandi, Ataollah, Shahrokhi, Shayan, Shaikh, Masood Ali, Shamim, Muhammad Aaqib, Shamshirgaran, Mohammad Ali, Sharfaei, Sadaf, Sharifan, Amin, Sharifi, Azam, Sharma, Rajendra, Sharma, Saurab, Shashamo, Bereket Beyene, Shi, Linhong, Shigematsu, Mika, Shiri, Rahman, Shivarov, Velizar, Siddig, Emmanuel Edwar, Sinaei, Ehsan, Singh, Ambrish, Singh, Jasvinder A., Singh, Paramdeep, Singh, Surjit, Singla, Shweta, Siraj, Md Shahjahan, Skryabina, Anna Aleksandrovna, Solanki, Ranjan, Solomon, Yonatan, Starodubova, Antonina V., Swain, Chandan Kumar, Talic, Stella, Tat, Nathan Y., Temsah, Mohamad-Hani, Terefa, Dufera Rikitu, Tesler, Riki, Thapar, Rekha, Tharwat, Samar, Thayakaran, Rasiah, Ticoalu, Jansje Henny Vera, Tovani-Palone, Marcos Roberto, Tusa, Biruk Shalmeno, Ty, Sree Sudha, Udoakang, Aniefiok John, Vahabi, Seyed Mohammad, Valizadeh, Rohollah, Van den Eynde, Jef, Varthya, Shoban Babu, Vasankari, Tommi Juhani, Venketasubramanian, Narayanaswamy, Villafañe, Jorge Hugo, Vlassov, Vasily, Vo, Anh Truc, Vu, Linh Gia, Wang, Yuan-Pang, Wiangkham, Taweewat, Wickramasinghe, Nuwan Darshana, Winkler, Andrea Sylvia, Wu, Ai-Min, Yadollahpour, Ali, Yahya, Galal, Yonemoto, Naohiro, You, Yuyi, Younis, Mustafa Z., Zakham, Fathiah, Zangiabadian, Moein, Zarrintan, Armin, Zhong, Chenwen, Zhou, Hengxing, Zhu, Zhaochen, Zielińska, Magdalena, Zikarg, Yossef Teshome, Zitoun, Osama A., Zoladl, Mohammad, Tam, Lai-Shan, and Wu, Dongze

Published
2024
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16. Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders

Author

Lim, David K., Rashid, Naim U., Oliva, Junier B., and Ibrahim, Joseph G.

Subjects
Computer Science - Machine Learning, Statistics - Applications
Abstract

Deep Learning (DL) methods have dramatically increased in popularity in recent years. While its initial success was demonstrated in the classification and manipulation of image data, there has been significant growth in the application of DL methods to problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of Variational Autoencoders (VAEs), a popular unsupervised DL architecture commonly utilized for dimension reduction, imputation, and learning latent representations of complex data. We propose a new VAE architecture, NIMIWAE, that is one of the first to flexibly account for both ignorable and non-ignorable patterns of missingness in input features at training time. Following training, samples can be drawn from the approximate posterior distribution of the missing data can be used for multiple imputation, facilitating downstream analyses on high dimensional incomplete datasets. We demonstrate through statistical simulation that our method outperforms existing approaches for unsupervised learning tasks and imputation accuracy. We conclude with a case study of an EHR dataset pertaining to 12,000 ICU patients containing a large number of diagnostic measurements and clinical outcomes, where many features are only partially observed., Comment: 31 pages, 4 figures, 2 tables, under review (Biometrics Methodology)

Published
2021

25. Grothendieck's Existence Theorem for Relatively Perfect Complexes on Algebraic Stacks

Author

Lim, David Benjamin

Subjects
Mathematics - Algebraic Geometry
Abstract

We prove Grothendieck's existence theorem for relatively perfect complexes on an algebraic stack that is proper and flat over an $I$-adically complete Noetherian ring $A$. This generalizes an earlier result of Lieblich in the setting of algebraic spaces., Comment: Removed flatness hypothesis from Lemma 3.4

Published
2019

26. To remove, or not to remove, both breasts

Author

Giannakeas, Vasily, Narod, Steven, and Lim, David

Subjects
Mastectomy -- Methods -- Patient outcomes, Breast cancer -- Diagnosis -- Care and treatment -- Genetic aspects, General interest, News, opinion and commentary
Abstract

Byline: VASILY GIANNAKEAS, STEVEN NAROD, DAVID LIM Dr. Vasily Giannakeas is a scientist at the Women's College Hospital (WCH) Research and Innovation Institute. Dr. Steven Narod is a senior scientist [...]

Published
2024

31. The Learn2Reg 2021 MICCAI Grand Challenge (PIMed Team)

Author

Shao, Wei, Vesal, Sulaiman, Lim, David, Li, Cynthia, Golestani, Negar, Alsinan, Ahmed, Fan, Richard, Sonn, Geoffrey, Rusu, Mirabela, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Aubreville, Marc, editor, Zimmerer, David, editor, and Heinrich, Mattias, editor

Published
2022
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32. A Machine Learning-Based Data Fusion Model for Online Traffic Violations Analysis

Author

Mostafa, Salama A., Mustapha, Aida, Ramli, Azizul Azhar, Fudzee, Mohd Farhan M. D., Lim, David, Kashinath, Shafiza Ariffin, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Khanna, Ashish, editor, Gupta, Deepak, editor, Bhattacharyya, Siddhartha, editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2022
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33. Two-Year Outcomes for Tricuspid Repair With a Transcatheter Edge-to-Edge Valve Repair From the Transatlantic TRILUMINATE Trial

Author

von Bardeleben, Ralph Stephan, Lurz, Philipp, Sorajja, Paul, Ruf, Tobias, Hausleiter, Jörg, Sitges, Marta, Da Rocha e Silva, Jaqueline, Näbauer, Michael, Weber, Marcel, Tang, Gilbert H.L., Heitkemper, Megan, Ying, Shih-Wa, Trochu, Jean-Noel, Kar, Saibal, Hahn, Rebecca T., Nickenig, Georg, Nickenig, Georg, von Bardeleben, Ralph Stephan, Geyer, Martin, Wenaweser, Peter, Biaggi, Patric, Kapos, Ioannis, Alsidawi, Said, Lurz, Philipp, Arzamendi, Dabit, Ander, Regueiro, Sabate, Manuel, Lala-Trindade, Anuradha, Adams, David, Swain, Julie, Orban, Mathias, Hausleiter, Jörg, Alfieri, Ottavio, Agricola, Eustachio, Montorfano, Matteo, Sinning, Jan-Malte, Tamm, Alexander, Hoffmann, Steffen, Kowalski, Marek, Garatti, Andrea, Pecoraro, Alessandra, Motta, Gessica, Tusa, Maurizio, Zuber, Michel, Windecker, Stephan, Samara, Michael, Mudy, Karol, Parikh, Sachin, Dee Wang, Dee, Piriou, Nicolas, Rommel, Karl-Philipp, Binner, Christian, Jimenez, Gustavo, Freixa, Xavier, Serra, Antonio, Lim, David, Lerakis, Stamatios, Buzzatti, Nicola, Baragagna, Marta, Tiburtius, Claudia, Grasso, Carmelo, De Marco, Federico, Maisano, Francesco, Taramasso, Maurizio, Bae, Richard, Frisoli, Tiberio, Guerin, Patrice, Hartung, Philipp, Mitter, Sumeet, Moss, Noah, Stocker, Thomas, Ancona, Francesco, Agricola, Eustachio, and Capogrosso, Cristina

Published
2023
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37. Pesticides and prostate cancer incidence and mortality: An environment‐wide association study.

Author

Soerensen, Simon John Christoph, Lim, David S., Montez‐Rath, Maria E., Chertow, Glenn M., Chung, Benjamin I., Rehkopf, David H., and Leppert, John T.

Subjects
*CANCER-related mortality, *AGRICULTURE, *PESTICIDES, *THIAMETHOXAM, *DISEASE risk factors, *PROSTATE cancer
Abstract

Background Methods Results Conclusions Prostate cancer is the most common cancer among men in the United States, yet modifiable risk factors remain elusive. In this study, the authors investigated the potential role of agricultural pesticide exposure in prostate cancer incidence and mortality.For this environment‐wide association study (EWAS), linear regression was used to analyze county‐level associations between the annual use of 295 distinct pesticides (measured in kg per county) and prostate cancer incidence and mortality rates in the contiguous United States. Data were analyzed in two cohorts: 1997–2001 pesticide use with 2011–2015 outcomes (discovery) and 2002–2006 use with 2016–2020 outcomes (replication). The reported effect sizes highlight how a 1‐standard‐deviation increase in log‐transformed pesticide use (kg per county) corresponds to changes in incidence. Analyses were adjusted for county‐level demographics, agricultural data, and multiple testing.Twenty‐two pesticides showed consistent, direct associations with prostate cancer incidence across both cohorts. Of these, four pesticides were also associated with prostate cancer mortality. In the replication cohort, each 1‐standard‐deviation increase in log‐transformed pesticide use corresponded to incidence increases per 100,000 individuals (trifluralin, 6.56 [95% confidence interval (CI), 5.04–8.07]; cloransulam‐methyl, 6.18 [95% CI, 4.06–8.31]; diflufenzopyr, 3.20 [95% CI, 1.09–5.31]; and thiamethoxam, 2.82 [95% CI, 1.14–4.50]). Limitations included ecological study design, potential unmeasured confounding, and lack of individual‐level exposure data.The results of this study suggest a potential link between certain pesticides and increased prostate cancer incidence and mortality. These findings warrant further investigation of these specific pesticides to confirm their role in prostate cancer risk and to develop potential public health interventions. [ABSTRACT FROM AUTHOR]

Published
2024
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39. What clinicians could consider in providing group interventions for people with cognitive impairments: a scoping review.

Author

Liu, Karen P.Y., Conroy, Mannix, Clark, Annemieke, and Lim, David

Subjects
COGNITION disorders treatment, MEDICAL information storage & retrieval systems, CINAHL database, GROUP psychotherapy, TREATMENT effectiveness, META-analysis, DESCRIPTIVE statistics, ALLIED health personnel, SYSTEMATIC reviews, MEDLINE, QUALITY of life, PEOPLE with disabilities
Abstract

This study identified evidence and considerations for allied health clinicians in providing group interventions for people with cognitive impairment. A scoping review was conducted by searching the MEDLINE (Ovid), CINHAL (EBSCOhost), Scopus (Elsevier), Embase (Ovid) and TROVE databases from 2016. Articles of any study design in which group interventions were performed by an allied health professional with participants with cognitive impairment were included. Data on physical, cognitive, psychological, and quality of life measures were extracted from the selected articles. Standardised mean changes (SMC) were calculated. Ten articles were included in the study. No article directly compared group interventions versus one‐to‐one interventions. The results of the meta‐analysis showed significant improvements after the intervention in the physical (SMC = 0.42, P = 0.013), cognitive (SMC = 0.43, P = 0.005), psychological (SMC = 0.14, P = 0.005), and quality of life domains (SMC = 0.28, P = 0.002). This review identified considerations for clinicians when developing group interventions for people with cognitive impairments, including specific participant criteria, increasing support, modifications to intervention difficulty, and environmental considerations. Group intervention for people with cognitive impairments demonstrated moderate effectiveness in improving physical and cognitive domains and a small effect in improving psychological and quality of life domains. Specific considerations are recommended when clinicians provide group interventions for people with cognitive impairments. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Approach to the Patient With Suspected Silver-Russell Syndrome.

Author

Kurup, Uttara, Lim, David B N, Palau, Helena, Maharaj, Avinaash V, Ishida, Miho, Davies, Justin H, and Storr, Helen L

Subjects
DELAYED diagnosis, GENETIC techniques, ETIOLOGY of diseases, GENETIC disorder diagnosis, PATIENTS' families
Abstract

Silver-Russell syndrome (SRS) is a clinical diagnosis requiring the fulfillment of ≥ 4/6 Netchine-Harbison Clinical Scoring System (NH-CSS) criteria. A score of ≥ 4/6 NH-CSS (or ≥ 3/6 with strong clinical suspicion) warrants (epi)genetic confirmation, identifiable in ∼60% patients. The approach to the investigation and diagnosis of SRS is detailed in the only international consensus guidance, published in 2016. In the intervening years, the clinical, biochemical, and (epi)genetic characteristics of SRS have rapidly expanded, largely attributable to advancing molecular genetic techniques and a greater awareness of related disorders. The most common etiologies of SRS remain loss of methylation of chromosome 11p15 (11p15LOM) and maternal uniparental disomy of chromosome 7 (upd(7)mat). Rarer causes of SRS include monogenic pathogenic variants in imprinted (CDKN1 C and IGF2) and non-imprinted (PLAG1 and HMGA2) genes. Although the age-specific NH-CSS can identify more common molecular causes of SRS, its use in identifying monogenic causes is unclear. Preliminary data suggest that NH-CSS is poor at identifying many of these cases. Additionally, there has been increased recognition of conditions with phenotypes overlapping with SRS that may fulfill NH-CSS criteria but have distinct genetic etiologies and disease trajectories. This group of conditions is frequently overlooked and under-investigated, leading to no or delayed diagnosis. Like SRS, these conditions are multisystemic disorders requiring multidisciplinary care and tailored management strategies. Early identification is crucial to improve outcomes and reduce the major burden of the diagnostic odyssey for patients and families. This article aims to enable clinicians to identify key features of rarer causes of SRS and conditions with overlapping phenotypes, show a logical approach to the molecular investigation, and highlight the differences in clinical management strategies. [ABSTRACT FROM AUTHOR]

Published
2024
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