Abstract: The present disclosure provides fully human antibodies that specifically bind the spike (S) protein of the SARS-CoV-2 coronavirus with high affinity, or antigen-binding proteins derived from such antibodies, and uses thereof. Included are anti-spike protein antibodies, antibody fragments, and single-chain antibodies, that are coronavirus neutralizing antibodies, as well as pharmaceutical compositions that include such antibodies and antibody fragments. Methods for using the anti-spike protein antibodies include methods of treating or preventing infection with a coronavirus, such as the SARS-CoV-2 coronavirus, by administering an antibody or antibody fragment as disclosed herein, including by intranasal delivery. Methods and compositions for treating or preventing coronavirus infection by administering a composition that includes a nucleic acid construct that encodes a neutralizing antibody are also provided.

Abstract: The present disclosure provides antigen-binding proteins, such as fully human antibodies, that specifically bind the spike (S) protein of the SARS-CoV-2 coronavirus and uses thereof. In various embodiments, the anti-spike protein antibodies are neutralizing antibodies that prevent binding of the SARS-CoV-2 coronavirus to a target cell expressing the ACE2 protein. Included are anti-spike protein antibodies, antibody fragments, and single-chain antibodies, as well as pharmaceutical compositions that include such antibodies and antibody fragments. Also provided herein are nucleic acids and recombinant expression vectors that encode the anti-spike protein antibodies and antibody fragments disclosed herein and transgenic cells transected with such nucleic acids and expression vectors. Further provided are methods for preparing and using such anti-spike protein antibodies.

Abstract: An entity resolution system performs a method of resolving one or more candidate entities based on a data set. The entity resolution system has a machine learning module and a narrative module. The machine learning module generates a synthesized data set, the synthesized data set comprising similarity ratings for each entity feature. The narrative module applies a clustering analysis to determine one or more distances between the group of similarity ratings for each entity feature and one or more clusters associated with known relationships between entities, generates a narrative output based on one or more distances. The narrative output states at least one identified relationship between at least two entities of the plurality of candidate entities and a confidence score. The narrative engine also provides the narrative output to a user interface.

Abstract: An entity resolution system performs a method of resolving one or more candidate entities based on a data set. The entity resolution system has a rules-based module, a machine learning module, a narrative module, and an evaluation module. The rules-based module compares the first entity features to the second entity features and determines whether a rule identifies a relationship between the first entity and the second entity. The machine learning module rates a similarity of the first entity features and the second entity features. The narrative module generates a narrative output based on one or more of the rules-based module and the machine learning module, the narrative output stating an identified relationship between the first entity and the second entity. The evaluation module determines one or more metrics to apply feedback to the system.

Abstract: Provided herein are oral drug preparations including cyproterone acetate and ethinylestradiol, dosing regimen for the drug preparations, and methods of treating diseases. The methods provided include the administration of the oral drug preparation to treat one or more symptoms of hyperandrogenic conditions or hyperandrogenic activities, to provide contraception, and to reduce a risk of vascular thromboembolism (VTE) in a subject.

Abstract: A detection modeling system performs a distribution analysis to alert to model degradation. The detection modeling system may have a distribution analysis module configured to perform an alerting process in conjunction with a processing device. The distribution analysis module may receive a risk tolerance rating for alerting to degradation of an analytical model, and determine a threshold value for a model metric based on the risk tolerance rating. The model metric may be a measure of a parameter associated with the analytical model. The distribution analysis module may also monitor the analytical model for degradation using the threshold value for the model metric and model metric values and alert to model degradation of the analytical model based on the monitoring of the analytical model.

Abstract: Provided herein are oral drug preparations including cyproterone acetate and ethinylestradiol, dosing regimen for the drug preparations, and methods of treating diseases. The methods provided include the administration of the oral drug preparation to treat one or more symptoms of hyperandrogenic conditions or hyperandrogenic activities, to provide contraception, and to reduce a risk of vascular thromboembolism (VTE) in a subject.

Abstract: A detection modeling system for alerting to analytical model degradation has a processing device and a memory coupled to the processing device. The detection modeling system is configured to perform a distribution analysis on the selected detection model to determine a first health rating for the selected detection model, perform a survival analysis on the selected detection model to determine a second health rating for the selected detection model, generate an indicative score for the detection model based on the first health rating and the second health rating, and compare the indicative score to a threshold value and alert to model degradation based on the comparison.

Abstract: A detection modeling system for alerting to analytical model degradation has a processing device and a memory coupled to the processing device. The detection modeling system is configured to perform a distribution analysis on the selected detection model to determine a first health rating for the selected detection model, perform a survival analysis on the selected detection model to determine a second health rating for the selected detection model, generate an indicative score for the detection model based on the first health rating and the second health rating, and compare the indicative score to a threshold value and alert to model degradation based on the comparison.

Abstract: A detection modeling system has a processing device and a memory coupled to the processing device. The detection modeling system is configured to select a detection model for assessing model degradation, the detection model being an analytical model for analyzing data and identifying target events, perform a survival analysis on a plurality of like detection models, determine a survival metric for the selected detection model based on the survival analysis, and compare the survival metric to a threshold value and alerting to model degradation based on the comparison.

Abstract: A detection modeling system performs a distribution analysis to alert to model degradation. The detection modeling system may have a distribution analysis module configured to perform an alerting process in conjunction with a processing device. The distribution analysis module may receive a risk tolerance rating for alerting to degradation of an analytical model, and determine a threshold value for a model metric based on the risk tolerance rating. The model metric may be a measure of a parameter associated with the analytical model. The distribution analysis module may also monitor the analytical model for degradation using the threshold value for the model metric and model metric values and alert to model degradation of the analytical model based on the monitoring of the analytical model.

Abstract: A detection modeling system for alerting to analytical model degradation has a processing device and a memory coupled to the processing device. The detection modeling system is configured to perform a distribution analysis on a selected detection model to determine a first health rating for the selected detection model. The detection modeling system is further configured to perform a survival analysis on the selected detection model to determine a second health rating for the selected detection model. The detection modeling system further generates an indicative score for the detection model based on the first health rating, the second health rating, and an ensemble weighting of the first health rating and the second health rating, and compares the indicative score to a threshold value to alert to model degradation based on the comparison.

Abstract: A detection modeling system has a processing device and a memory coupled to the processing device. The detection modeling system is configured to obtain health value data associated with an analytical model, determine a time period at which the model was trained based on the obtained health value data, and identify a survival time period of the model based on the determined time period at which the model was trained and a failure time period of the model. The detection modeling system is further configured to repeat these steps to determine a survival time period for a plurality of analytical models, and perform a survival analysis based on the survival time period for the plurality of analytical models.

Abstract: A detection modeling system performs a distribution analysis to alert to model degradation. The detection modeling system may have a distribution analysis module configured to perform an alerting process in conjunction with a processing device. The distribution analysis module may select, by the processing device, model metrics for analysis, the model metrics being a measure of a parameter associated with the analytical model and determine normal distributions for model metric results for each of the selected model metrics. The detection modeling system may further receive model metric values for each of the selected model metric, compare, by the processing device, the model metric values to the normal distributions for model metric results for each of the received model metric value, and alert, by the processing device, to model degradation of the analytical model based on the comparison of the model metric values to the normal distributions for model metric results.

Abstract: An entity resolution system performs a method of resolving one or more candidate entities based on a data set. The entity resolution system has a rules-based module, a machine learning module, a narrative module, and an evaluation module. The rules-based module compares the first entity features to the second entity features and determines whether a rule identifies a relationship between the first entity and the second entity. The machine learning module rates a similarity of the first entity features and the second entity features. The narrative module generates a narrative output based on one or more of the rules-based module and the machine learning module, the narrative output stating an identified relationship between the first entity and the second entity. The evaluation module determines one or more metrics to apply feedback to the system.

Abstract: An entity resolution system performs a method of resolving one or more candidate entities based on a data set. The entity resolution system has a machine learning module and a narrative module. The machine learning module generates a synthesized data set, the synthesized data set comprising similarity ratings for each entity feature. The narrative module applies a clustering analysis to determine one or more distances between the group of similarity ratings for each entity feature and one or more clusters associated with known relationships between entities, generates a narrative output based on one or more distances. The narrative output states at least one identified relationship between at least two entities of the plurality of candidate entities and a confidence score. The narrative engine also provides the narrative output to a user interface.

Abstract: A method of treating pain, e.g., acute post-operative pain, by administering to a human patient(s) a therapeutically effective dose of tramadol intravenously in a dosing regimen which includes one or more loading doses administered at shortened intervals as compared to dosing at steady-state is disclosed. In preferred embodiments, the intravenous dosing regimen provides a Cmax and AUC of tramadol is similar to the Cmax and AUC of an oral dose of 100 mg tramadol HCl given every 6 hours.

Abstract: A method of treating pain, e.g., acute post-operative pain, by administering to a human patient(s) a therapeutically effective dose of tramadol intravenously in a dosing regimen which includes one or more loading doses administered at shortened intervals as compared to dosing at steady-state is disclosed. In certain embodiments, the dose of tramadol is from about 45 mg to about 80 mg and the second (and optionally) third doses are intravenously administered at intervals of from about 2 to about 3 hours, and thereafter the tramadol is intravenously administered at a dosing interval of about 4 to about 6 hours, until the patient no longer requires treatment with tramadol. In preferred embodiments, the intravenous dosing regimen provides a Cmax and AUC of tramadol is similar to the Cmax and AUC of an oral dose of 100 mg tramadol HCl given every 6 hours.

Abstract: A method of treating pain, e.g., acute post-operative pain, by intravenously administering to a human patient(s) a dose of tramadol of about 45 mg to about 80 mg, based on tramadol hydrochloride, together with an intravenous dose of a therapeutically effective amount of ketorolac about every 6 hours.

Abstract: A method of treating pain, e.g., acute post-operative pain, by administering to a human patient(s) a therapeutically effective dose of tramadol intravenously in a dosing regimen which includes one or more loading doses administered at shortened intervals as compared to dosing at steady-state is disclosed. In preferred embodiments, the intravenous dosing regimen provides a Cmax and AUC of tramadol is similar to the Cmax and AUC of an oral dose of 100 mg tramadol HCl given every 6 hours. In certain preferred embodiments, the dosing regimen comprises 50 mg IV tramadol at Hour 0, followed by 50 mg at Hour 2, 50 mg at hour 4, and 50 mg every 4 hours thereafter (e.g., until the patient no longer requires treatment with tramadol).