ABSTRACT
Background: Tracking of various parameters associated with laser hair removal is tedious and time consuming. The currently available mathematical models are not simple enough for physicians to be used as a treatment optimization and patient education tool. Aim: The aim of the study was to develop a mathematical model for laser hair removal using agent-based modeling and to make a user-friendly simulation environment. Methods: The model was created using NetLogo. The hairs were modeled as agents oscillating between anagen and telogen. The variables were assigned based on published data whenever possible and the various paths the agent could take were coded as conditional statements. The improvement was assessed using an arbitrary index which takes into account the mean diameter and pigmentation along with the number and length of hairs visible above the surface. Few of the commonly encountered scenarios were simulated using the model. Results: The model is made freely available online (http://www.gulfdoctor.net/model/lhr.htm). Limited number of simulations performed indicated that an eight-week gap between laser sessions may be more effective than a four-week gap. Conclusions: The simulation provides a reliable tool for treatment optimization and patient education as obtaining relevant clinical data is slow and labor-intensive. Its visual interface and online availability makes it useful for everyday use.
ABSTRACT
Personal Digital Assistants (PDAs) have become a part of everyday life. DermaMan is a freely available, Java-based, dermatology-specific calculator for handheld devices. It includes modules to compute PASI, MASI, SCORAD, and for calculations related to topical PUVA and Botox (R) administration.
Subject(s)
Botulinum Toxins, Type A/administration & dosage , Computers, Handheld , Dermatology/instrumentation , Diagnosis, Computer-Assisted , Drug Therapy, Computer-Assisted , Ficusin/administration & dosage , Humans , Melanosis/pathology , Neuromuscular Agents/administration & dosage , PUVA Therapy , Programming Languages , Psoriasis/physiopathology , Severity of Illness Index , SoftwareABSTRACT
Majority of technical writing is done by groups of experts and various web based applications have made this collaboration easy. Email exchange of word processor documents with tracked changes used to be the standard technique for collaborative writing. However web based tools like Google docs and Spreadsheets have made the process fast and efficient. Various versioning tools and synchronous editors are available for those who need additional functionality. Having a group leader who decides the scheduling, communication and conflict resolving protocols is important for successful collaboration.
Subject(s)
Computer Graphics , Database Management Systems , Databases, Bibliographic , Electronic Mail , Humans , Software , Word Processing , WritingABSTRACT
EndNote is a useful software for online literature search and efficient bibliography management. It helps to format the bibliography according to the citation style of each journal. EndNote stores references in a library file, which can be shared with others. It can connect to online resources like PubMed and retrieve search results as per the search criteria. It can also effortlessly integrate with popular word processors like MS Word. The Indian Journal of Dermatology, Venereology and Leprology website has a provision to import references to EndNote.
Subject(s)
Database Management Systems , Databases, Bibliographic , Information Storage and Retrieval/methods , SoftwareABSTRACT
BACKGROUND AND AIMS: There are various genotypic variations known for the four phenotypic presentations of epidermolysis bullosa simplex (EBS). A neural network algorithm may be used to find the relationship between the various factors responsible for a particular phenotypic presentation. We assessed the value of neural network to predict the prognosis of epidermolysis bullosa simplex. METHODS: Cases of EBS in which sequencing had been performed to identify the position and type of mutation were collected by literature search and the resulting data was analyzed using neural network algorithm. RESULTS: The statistical prediction had an accuracy rate of 78%. CONCLUSION: Neural networks can identify hidden patterns in a huge database without the intervention of a skilled statistician. It has the potential to change the way we analyze clinical and experimental data at present.